Posted: November 23rd, 2022

Info Tech in Global Economy

Q1: In the chapter 2, both the authors, (Ahrweiler, P., and Gilbert, N.) suggested that there was a need for quality simulation modeling education.  In addition, the authors identified two types of public servants that need this education.  Name the two types of public servants, and briefly explain why it’s important for these two types of servants to receive this education?

a. identify and name the two types of public servants,  

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b. provide a brief narrative for each to support your answer

Q2: Chapter 3 deals with the assessment of the quality of the simulation.  The first section of the chapter pointed out the problems of the standard view and the constructivist view in evaluating social simulations. Simulation is good when we get from it what we originally would have liked to get from the target; in this, the evaluation of the simulation is guided by the expectations, anticipations, and experience of the community that uses it. This makes the user community view the most promising mechanism to assess the quality of a policy-modeling exercise. The second section looks at a concrete policy-modeling example to test this idea. 

Based on the author’s assessment on the quality of social simulation modeling, 

a. why did they think that the user community needed to be enabled to understand the model?  

b. identify and name the reason why the user community needed to be enabled to understand the model,  

Public Administration and Information
Technology
Volume 10
Series Editor
Christopher G. Reddick
San Antonio, Texas, USA
w.jager@rug.nl

More information about this series at http://www.springer.com/series/10796
w.jager@rug.nl

Marijn Janssen • Maria A. Wimmer
Ameneh Deljoo
Editors
Policy Practice and Digital
Science
Integrating Complex Systems, Social
Simulation and Public Administration
in Policy Research
2123
w.jager@rug.nl

Editors
Marijn Janssen Ameneh Deljoo
Faculty of Technology, Policy, and Faculty of Technology, Policy, and
Management Management
Delft University of Technology Delft University of Technology
Delft Delft
The Netherlands The Netherlands
Maria A. Wimmer
Institute for Information Systems Research
University of Koblenz-Landau
Koblenz
Germany
ISBN 978-3-319-12783-5 ISBN 978-3-319-12784-2 (eBook)
Public Administration and Information Technology
DOI 10.1007/978-3-319-12784-2
Library of Congress Control Number: 2014956771
Springer Cham Heidelberg New York London
© Springer International Publishing Switzerland 2015
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the
material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation,
broadcasting, reproduction on microfilms or in any other physical way, and transmission or information
storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology
now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication
does not imply, even in the absence of a specific statement, that such names are exempt from the relevant
protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this book
are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the
editors give a warranty, express or implied, with respect to the material contained herein or for any errors
or omissions that may have been made.
Printed on acid-free paper
Springer is part of Springer Science+Business Media (www.springer.com)
w.jager@rug.nl

Preface
The last economic and financial crisis has heavily threatened European and other
economies around the globe. Also, the Eurozone crisis, the energy and climate
change crises, challenges of demographic change with high unemployment rates,
and the most recent conflicts in the Ukraine and the near East or the Ebola virus
disease in Africa threaten the wealth of our societies in different ways. The inability
to predict or rapidly deal with dramatic changes and negative trends in our economies
and societies can seriously hamper the wealth and prosperity of the European Union
and its Member States as well as the global networks. These societal and economic
challenges demonstrate an urgent need for more effective and efficient processes of
governance and policymaking, therewith specifically addressing crisis management
and economic/welfare impact reduction.
Therefore, investing in the exploitation of innovative information and commu-
nication technology (ICT) in the support of good governance and policy modeling
has become a major effort of the European Union to position itself and its Member
States well in the global digital economy. In this realm, the European Union has
laid out clear strategic policy objectives for 2020 in the Europe 2020 strategy1: In
a changing world, we want the EU to become a smart, sustainable, and inclusive
economy. These three mutually reinforcing priorities should help the EU and the
Member States deliver high levels of employment, productivity, and social cohesion.
Concretely, the Union has set five ambitious objectives—on employment, innovation,
education, social inclusion, and climate/energy—to be reached by 2020. Along with
this, Europe 2020 has established four priority areas—smart growth, sustainable
growth, inclusive growth, and later added: A strong and effective system of eco-
nomic governance—designed to help Europe emerge from the crisis stronger and to
coordinate policy actions between the EU and national levels.
To specifically support European research in strengthening capacities, in overcom-
ing fragmented research in the field of policymaking, and in advancing solutions for
1 Europe 2020 http://ec.europa.eu/europe2020/index_en.htm
v
w.jager@rug.nl

vi Preface
ICT supported governance and policy modeling, the European Commission has co-
funded an international support action called eGovPoliNet2. The overall objective
of eGovPoliNet was to create an international, cross-disciplinary community of re-
searchers working on ICT solutions for governance and policy modeling. In turn,
the aim of this community was to advance and sustain research and to share the
insights gleaned from experiences in Europe and globally. To achieve this, eGovPo-
liNet established a dialogue, brought together experts from distinct disciplines, and
collected and analyzed knowledge assets (i.e., theories, concepts, solutions, findings,
and lessons on ICT solutions in the field) from different research disciplines. It built
on case material accumulated by leading actors coming from distinct disciplinary
backgrounds and brought together the innovative knowledge in the field. Tools, meth-
ods, and cases were drawn from the academic community, the ICT sector, specialized
policy consulting firms as well as from policymakers and governance experts. These
results were assembled in a knowledge base and analyzed in order to produce com-
parative analyses and descriptions of cases, tools, and scientific approaches to enrich
a common knowledge base accessible via www.policy-community.eu.
This book, entitled “Policy Practice and Digital Science—Integrating Complex
Systems, Social Simulation, and Public Administration in Policy Research,” is one
of the exciting results of the activities of eGovPoliNet—fusing community building
activities and activities of knowledge analysis. It documents findings of comparative
analyses and brings in experiences of experts from academia and from case descrip-
tions from all over the globe. Specifically, it demonstrates how the explosive growth
in data, computational power, and social media creates new opportunities for policy-
making and research. The book provides a first comprehensive look on how to take
advantage of the development in the digital world with new approaches, concepts,
instruments, and methods to deal with societal and computational complexity. This
requires the knowledge traditionally found in different disciplines including public
administration, policy analyses, information systems, complex systems, and com-
puter science to work together in a multidisciplinary fashion and to share approaches.
This book provides the foundation for strongly multidisciplinary research, in which
the various developments and disciplines work together from a comprehensive and
holistic policymaking perspective. A wide range of aspects for social and professional
networking and multidisciplinary constituency building along the axes of technol-
ogy, participative processes, governance, policy modeling, social simulation, and
visualization are tackled in the 19 papers.
With this book, the project makes an effective contribution to the overall objec-
tives of the Europe 2020 strategy by providing a better understanding of different
approaches to ICT enabled governance and policy modeling, and by overcoming the
fragmented research of the past. This book provides impressive insights into various
theories, concepts, and solutions of ICT supported policy modeling and how stake-
holders can be more actively engaged in public policymaking. It draws conclusions
2 eGovPoliNet is cofunded under FP 7, Call identifier FP7-ICT-2011-7, URL: www.policy-
community.eu
w.jager@rug.nl

Preface vii
of how joint multidisciplinary research can bring more effective and resilient find-
ings for better predicting dramatic changes and negative trends in our economies and
societies.
It is my great pleasure to provide the preface to the book resulting from the
eGovPoliNet project. This book presents stimulating research by researchers coming
from all over Europe and beyond. Congratulations to the project partners and to the
authors!—Enjoy reading!
Thanassis Chrissafis
Project officer of eGovPoliNet
European Commission
DG CNECT, Excellence in Science, Digital Science
w.jager@rug.nl

Contents
1 Introduction to Policy-Making in the Digital Age . . . . . . . . . . . . . . . . . 1
Marijn Janssen and Maria A. Wimmer
2 Educating Public Managers and Policy Analysts
in an Era of Informatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Christopher Koliba and Asim Zia
3 The Quality of Social Simulation: An Example from Research
Policy Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Petra Ahrweiler and Nigel Gilbert
4 Policy Making and Modelling in a Complex World . . . . . . . . . . . . . . . . 57
Wander Jager and Bruce Edmonds
5 From Building a Model to Adaptive Robust Decision Making
Using Systems Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Erik Pruyt
6 Features and Added Value of Simulation Models Using Different
Modelling Approaches Supporting Policy-Making: A Comparative
Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
Dragana Majstorovic, Maria A.Wimmer, Roy Lay-Yee, Peter Davis
and Petra Ahrweiler
7 A Comparative Analysis of Tools and Technologies
for Policy Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
Eleni Kamateri, Eleni Panopoulou, Efthimios Tambouris,
Konstantinos Tarabanis, Adegboyega Ojo, Deirdre Lee
and David Price
8 Value Sensitive Design of Complex Product Systems . . . . . . . . . . . . . . . 157
Andreas Ligtvoet, Geerten van de Kaa, Theo Fens, Cees van Beers,
Paulier Herder and Jeroen van den Hoven
ix
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x Contents
9 Stakeholder Engagement in Policy Development: Observations
and Lessons from International Experience . . . . . . . . . . . . . . . . . . . . . . 177
Natalie Helbig, Sharon Dawes, Zamira Dzhusupova, Bram Klievink
and Catherine Gerald Mkude
10 Values in Computational Models Revalued . . . . . . . . . . . . . . . . . . . . . . . 205
Rebecca Moody and Lasse Gerrits
11 The Psychological Drivers of Bureaucracy: Protecting
the Societal Goals of an Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221
Tjeerd C. Andringa
12 Active and Passive Crowdsourcing in Government . . . . . . . . . . . . . . . . 261
Euripidis Loukis and Yannis Charalabidis
13 Management of Complex Systems: Toward Agent-Based
Gaming for Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291
Wander Jager and Gerben van der Vegt
14 The Role of Microsimulation in the Development of Public Policy . . . 305
Roy Lay-Yee and Gerry Cotterell
15 Visual Decision Support for Policy Making: Advancing Policy
Analysis with Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321
Tobias Ruppert, Jens Dambruch, Michel Krämer, Tina Balke, Marco
Gavanelli, Stefano Bragaglia, Federico Chesani, Michela Milano
and Jörn Kohlhammer
16 Analysis of Five Policy Cases in the Field of Energy Policy . . . . . . . . . 355
Dominik Bär, Maria A.Wimmer, Jozef Glova, Anastasia
Papazafeiropoulou and Laurence Brooks
17 Challenges to Policy-Making in Developing Countries
and the Roles of Emerging Tools, Methods and Instruments:
Experiences from Saint Petersburg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379
Dmitrii Trutnev, Lyudmila Vidyasova and Andrei Chugunov
18 Sustainable Urban Development, Governance and Policy:
A Comparative Overview of EU Policies and Projects . . . . . . . . . . . . . 393
Diego Navarra and Simona Milio
19 eParticipation, Simulation Exercise and Leadership Training
in Nigeria: Bridging the Digital Divide . . . . . . . . . . . . . . . . . . . . . . . . . . . 417
Tanko Ahmed
w.jager@rug.nl

Contributors
Tanko Ahmed National Institute for Policy and Strategic Studies (NIPSS), Jos,
Nigeria
Petra Ahrweiler EA European Academy of Technology and Innovation Assess-
ment GmbH, Bad Neuenahr-Ahrweiler, Germany
Tjeerd C. Andringa University College Groningen, Institute of Artificial In-
telligence and Cognitive Engineering (ALICE), University of Groningen, AB,
Groningen, the Netherlands
Tina Balke University of Surrey, Surrey, UK
Dominik Bär University of Koblenz-Landau, Koblenz, Germany
Cees van Beers Faculty of Technology, Policy, and Management, Delft University
of Technology, Delft, The Netherlands
Stefano Bragaglia University of Bologna, Bologna, Italy
Laurence Brooks Brunel University, Uxbridge, UK
Yannis Charalabidis University of the Aegean, Samos, Greece
Federico Chesani University of Bologna, Bologna, Italy
Andrei Chugunov ITMO University, St. Petersburg, Russia
Gerry Cotterell Centre of Methods and Policy Application in the Social Sciences
(COMPASS Research Centre), University of Auckland, Auckland, New Zealand
Jens Dambruch Fraunhofer Institute for Computer Graphics Research, Darmstadt,
Germany
Peter Davis Centre of Methods and Policy Application in the Social Sciences
(COMPASS Research Centre), University of Auckland, Auckland, New Zealand
Sharon Dawes Center for Technology in Government, University at Albany,
Albany, New York, USA
xi
w.jager@rug.nl

xii Contributors
Zamira Dzhusupova Department of Public Administration and Development Man-
agement, United Nations Department of Economic and Social Affairs (UNDESA),
NewYork, USA
Bruce Edmonds Manchester Metropolitan University, Manchester, UK
Theo Fens Faculty of Technology, Policy, and Management, Delft University of
Technology, Delft, The Netherlands
Marco Gavanelli University of Ferrara, Ferrara, Italy
Lasse Gerrits Department of Public Administration, Erasmus University
Rotterdam, Rotterdam, The Netherlands
Nigel Gilbert University of Surrey, Guildford, UK
Jozef Glova Technical University Kosice, Kosice, Slovakia
Natalie Helbig Center for Technology in Government, University at Albany,
Albany, New York, USA
Paulier Herder Faculty of Technology, Policy, and Management, Delft University
of Technology, Delft, The Netherlands
Jeroen van den Hoven Faculty of Technology, Policy, and Management, Delft
University of Technology, Delft, The Netherlands
Wander Jager Groningen Center of Social Complexity Studies, University of
Groningen, Groningen, The Netherlands
Marijn Janssen Faculty of Technology, Policy, and Management, Delft University
of Technology, Delft, The Netherlands
Geerten van de Kaa Faculty of Technology, Policy, and Management, Delft
University of Technology, Delft, The Netherlands
Eleni Kamateri Information Technologies Institute, Centre for Research &
Technology—Hellas, Thessaloniki, Greece
Bram Klievink Faculty of Technology, Policy and Management, Delft University
of Technology, Delft, The Netherlands
Jörn Kohlhammer GRIS, TU Darmstadt & Fraunhofer IGD, Darmstadt, Germany
Christopher Koliba University of Vermont, Burlington, VT, USA
Michel Krämer Fraunhofer Institute for Computer Graphics Research, Darmstadt,
Germany
Roy Lay-Yee Centre of Methods and Policy Application in the Social Sciences
(COMPASS Research Centre), University of Auckland, Auckland, New Zealand
Deirdre Lee INSIGHT Centre for Data Analytics, NUIG, Galway, Ireland
w.jager@rug.nl

Contributors xiii
Andreas Ligtvoet Faculty of Technology, Policy, and Management, Delft Univer-
sity of Technology, Delft, The Netherlands
Euripidis Loukis University of the Aegean, Samos, Greece
Dragana Majstorovic University of Koblenz-Landau, Koblenz, Germany
Michela Milano University of Bologna, Bologna, Italy
Simona Milio London School of Economics, Houghton Street, London, UK
Catherine Gerald Mkude Institute for IS Research, University of Koblenz-Landau,
Koblenz, Germany
Rebecca Moody Department of Public Administration, Erasmus University
Rotterdam, Rotterdam, The Netherlands
Diego Navarra Studio Navarra, London, UK
Adegboyega Ojo INSIGHT Centre for Data Analytics, NUIG, Galway, Ireland
Eleni Panopoulou Information Technologies Institute, Centre for Research &
Technology—Hellas, Thessaloniki, Greece
Anastasia Papazafeiropoulou Brunel University, Uxbridge, UK
David Price Thoughtgraph Ltd, Somerset, UK
Erik Pruyt Faculty of Technology, Policy, and Management, Delft University of
Technology, Delft, The Netherlands; Netherlands Institute for Advanced Study,
Wassenaar, The Netherlands
Tobias Ruppert Fraunhofer Institute for Computer Graphics Research, Darmstadt,
Germany
Efthimios Tambouris Information Technologies Institute, Centre for Research &
Technology—Hellas, Thessaloniki, Greece; University of Macedonia, Thessaloniki,
Greece
Konstantinos Tarabanis Information Technologies Institute, Centre for Research
& Technology—Hellas, Thessaloniki, Greece; University of Macedonia, Thessa-
loniki, Greece
Dmitrii Trutnev ITMO University, St. Petersburg, Russia
Gerben van der Vegt Faculty of Economics and Business, University of Groningen,
Groningen, The Netherlands
Lyudmila Vidyasova ITMO University, St. Petersburg, Russia
Maria A. Wimmer University of Koblenz-Landau, Koblenz, Germany
Asim Zia University of Vermont, Burlington, VT, USA
w.jager@rug.nl

Chapter 1
Introduction to Policy-Making in the Digital Age
Marijn Janssen and Maria A. Wimmer
We are running the 21st century using 20th century systems on
top of 19th century political structures. . . .
John Pollock, contributing editor MIT technology review
Abstract The explosive growth in data, computational power, and social media
creates new opportunities for innovating governance and policy-making. These in-
formation and communications technology (ICT) developments affect all parts of
the policy-making cycle and result in drastic changes in the way policies are devel-
oped. To take advantage of these developments in the digital world, new approaches,
concepts, instruments, and methods are needed, which are able to deal with so-
cietal complexity and uncertainty. This field of research is sometimes depicted
as e-government policy, e-policy, policy informatics, or data science. Advancing
our knowledge demands that different scientific communities collaborate to create
practice-driven knowledge. For policy-making in the digital age disciplines such as
complex systems, social simulation, and public administration need to be combined.
1.1 Introduction
Policy-making and its subsequent implementation is necessary to deal with societal
problems. Policy interventions can be costly, have long-term implications, affect
groups of citizens or even the whole country and cannot be easily undone or are even
irreversible. New information and communications technology (ICT) and models
can help to improve the quality of policy-makers. In particular, the explosive growth
in data, computational power, and social media creates new opportunities for in-
novating the processes and solutions of ICT-based policy-making and research. To
M. Janssen (�)
Faculty of Technology, Policy, and Management, Delft University of Technology,
Delft, The Netherlands
e-mail: m.f.w.h.a.janssen@tudelft.nl
M. A. Wimmer
University of Koblenz-Landau, Koblenz, Germany
© Springer International Publishing Switzerland 2015 1
M. Janssen et al. (eds.), Policy Practice and Digital Science,
Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_1
w.jager@rug.nl

2 M. Janssen and M. A. Wimmer
take advantage of these developments in the digital world, new approaches, con-
cepts, instruments, and methods are needed, which are able to deal with societal and
computational complexity. This requires the use of knowledge which is traditionally
found in different disciplines, including (but not limited to) public administration,
policy analyses, information systems, complex systems, and computer science. All
these knowledge areas are needed for policy-making in the digital age. The aim of
this book is to provide a foundation for this new interdisciplinary field in which
various traditional disciplines are blended.
Both policy-makers and those in charge of policy implementations acknowledge
that ICT is becoming more and more important and is changing the policy-making
process, resulting in a next generation policy-making based on ICT support. The field
of policy-making is changing driven by developments such as open data, computa-
tional methods for processing data, opinion mining, simulation, and visualization of
rich data sets, all combined with public engagement, social media, and participatory
tools. In this respect Web 2.0 and even Web 3.0 point to the specific applications of
social networks and semantically enriched and linked data which are important for
policy-making. In policy-making vast amount of data are used for making predictions
and forecasts. This should result in improving the outcomes of policy-making.
Policy-making is confronted with an increasing complexity and uncertainty of the
outcomes which results in a need for developing policy models that are able to deal
with this. To improve the validity of the models policy-makers are harvesting data to
generate evidence. Furthermore, they are improving their models to capture complex
phenomena and dealing with uncertainty and limited and incomplete information.
Despite all these efforts, there remains often uncertainty concerning the outcomes of
policy interventions. Given the uncertainty, often multiple scenarios are developed
to show alternative outcomes and impact. A condition for this is the visualization of
policy alternatives and its impact. Visualization can ensure involvement of nonexpert
and to communicate alternatives. Furthermore, games can be used to let people gain
insight in what can happen, given a certain scenario. Games allow persons to interact
and to experience what happens in the future based on their interventions.
Policy-makers are often faced with conflicting solutions to complex problems,
thus making it necessary for them to test out their assumptions, interventions, and
resolutions. For this reason policy-making organizations introduce platforms facili-
tating policy-making and citizens engagements and enabling the processing of large
volumes of data. There are various participative platforms developed by government
agencies (e.g., De Reuver et al. 2013; Slaviero et al. 2010; Welch 2012). Platforms
can be viewed as a kind of regulated environment that enable developers, users, and
others to interact with each other, share data, services, and applications, enable gov-
ernments to more easily monitor what is happening and facilitate the development
of innovative solutions (Janssen and Estevez 2013). Platforms should provide not
only support for complex policy deliberations with citizens but should also bring to-
gether policy-modelers, developers, policy-makers, and other stakeholders involved
in policy-making. In this way platforms provide an information-rich, interactive
w.jager@rug.nl

1 Introduction to Policy-Making in the Digital Age 3
environment that brings together relevant stakeholders and in which complex phe-
nomena can be modeled, simulated, visualized, discussed, and even the playing of
games can be facilitated.
1.2 Complexity and Uncertainty in Policy-Making
Policy-making is driven by the need to solve societal problems and should result in
interventions to solve these societal problems. Examples of societal problems are
unemployment, pollution, water quality, safety, criminality, well-being, health, and
immigration. Policy-making is an ongoing process in which issues are recognized
as a problem, alternative courses of actions are formulated, policies are affected,
implemented, executed, and evaluated (Stewart et al. 2007). Figure 1.1 shows the
typical stages of policy formulation, implementation, execution, enforcement, and
evaluation. This process should not be viewed as linear as many interactions are
necessary as well as interactions with all kind of stakeholders. In policy-making
processes a vast amount of stakeholders are always involved, which makes policy-
making complex.
Once a societal need is identified, a policy has to be formulated. Politicians,
members of parliament, executive branches, courts, and interest groups may be
involved in these formulations. Often contradictory proposals are made, and the
impact of a proposal is difficult to determine as data is missing, models cannot
citizen
s
Policy formulation
Policy
implementation
Policy
execution
Policy
enforcement and
evaluation
politicians
Policy-
makers
Administrative
organizations
b
u
sin
esses
Inspection and
enforcement agencies
experts
Fig. 1.1 Overview of policy cycle and stakeholders
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4 M. Janssen and M. A. Wimmer
capture the complexity, and the results of policy models are difficult to interpret and
even might be interpreted in an opposing way. This is further complicated as some
proposals might be good but cannot be implemented or are too costly to implement.
There is a large uncertainty concerning the outcomes.
Policy implementation is done by organizations other than those that formulated
the policy. They often have to interpret the policy and have to make implemen-
tation decisions. Sometimes IT can block quick implementation as systems have
to be changed. Although policy-making is the domain of the government, private
organizations can be involved to some extent, in particular in the execution of policies.
Once all things are ready and decisions are made, policies need to be executed.
During the execution small changes are typically made to fine tune the policy formu-
lation, implementation decisions might be more difficult to realize, policies might
bring other benefits than intended, execution costs might be higher and so on. Typ-
ically, execution is continually changing. Evaluation is part of the policy-making
process as it is necessary to ensure that the policy-execution solved the initial so-
cietal problem. Policies might become obsolete, might not work, have unintended
affects (like creating bureaucracy) or might lose its support among elected officials,
or other alternatives might pop up that are better.
Policy-making is a complex process in which many stakeholders play a role. In
the various phases of policy-making different actors are dominant and play a role.
Figure 1.1 shows only some actors that might be involved, and many of them are not
included in this figure. The involvement of so many actors results in fragmentation
and often actors are even not aware of the decisions made by other actors. This makes
it difficult to manage a policy-making process as each actor has other goals and might
be self-interested.
Public values (PVs) are a way to try to manage complexity and give some guidance.
Most policies are made to adhere to certain values. Public value management (PVM)
represents the paradigm of achieving PVs as being the primary objective (Stoker
2006). PVM refers to the continuous assessment of the actions performed by public
officials to ensure that these actions result in the creation of PV (Moore 1995). Public
servants are not only responsible for following the right procedure, but they also have
to ensure that PVs are realized. For example, civil servants should ensure that garbage
is collected. The procedure that one a week garbage is collected is secondary. If it is
necessary to collect garbage more (or less) frequently to ensure a healthy environment
then this should be done. The role of managers is not only to ensure that procedures
are followed but they should be custodians of public assets and maximize a PV.
There exist a wide variety of PVs (Jørgensen and Bozeman 2007). PVs can be
long-lasting or might be driven by contemporary politics. For example, equal access
is a typical long-lasting value, whereas providing support for students at universities
is contemporary, as politicians might give more, less, or no support to students. PVs
differ over times, but also the emphasis on values is different in the policy-making
cycle as shown in Fig. 1.2. In this figure some of the values presented by Jørgensen
and Bozeman (2007) are mapped onto the four policy-making stages. Dependent on
the problem at hand other values might play a role that is not included in this figure.
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1 Introduction to Policy-Making in the Digital Age 5
Policy
formulation
Policy
implementation
Policy
execution
Policy
enforcement
and evaluation
efficiency
efficiency
accountability
transparancy
responsiveness
public interest
will of the people
listening
citizen involvement
evidence-based
protection of
individual rights
accountability
transparancy
evidence-based
equal access
balancing of interests
robust
honesty
fair
timelessness
reliable
flexible
fair
Fig. 1.2 Public values in the policy cycle
Policy is often formulated by politicians in consultation with experts. In the PVM
paradigm, public administrations aim at creating PVs for society and citizens. This
suggests a shift from talking about what citizens expect in creating a PV. In this view
public officials should focus on collaborating and creating a dialogue with citizens
in order to determine what constitutes a PV.
1.3 Developments
There is an infusion of technology that changes policy processes at both the individual
and group level. There are a number of developments that influence the traditional
way of policy-making, including social media as a means to interact with the public
(Bertot et al. 2012), blogs (Coleman and Moss 2008), open data (Janssen et al. 2012;
Zuiderwijk and Janssen 2013), freedom of information (Burt 2011), the wisdom
of the crowds (Surowiecki 2004), open collaboration and transparency in policy
simulation (Wimmer et al. 2012a, b), agent-based simulation and hybrid modeling
techniques (Koliba and Zia 2012) which open new ways of innovative policy-making.
Whereas traditional policy-making is executed by experts, now the public is involved
to fulfill requirements of good governance according to open government principles.
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6 M. Janssen and M. A. Wimmer
Also, the skills and capabilities of crowds can be explored and can lead to better and
more transparent democratic policy decisions. All these developments can be used for
enhancing citizen’s engagement and to involve citizens better in the policy-making
process. We want to emphasize three important developments.
1.3.1 The Availability of Big and Open Linked Data (BOLD)
Policy-making heavily depends on data about existing policies and situations to
make decisions. Both public and private organizations are opening their data for use
by others. Although information could be requested for in the past, governments
have changed their strategy toward actively publishing open data in formats that are
readily and easily accessible (for example, European_Commission 2003; Obama
2009). Multiple perspectives are needed to make use of and stimulate new practices
based on open data (Zuiderwijk et al. 2014). New applications and innovations can
be based solely on open data, but often open data are enriched with data from other
sources. As data can be generated and provided in huge amounts, specific needs for
processing, curation, linking, visualization, and maintenance appear. The latter is
often denoted with big data in which the value is generated by combining different
datasets (Janssen et al. 2014). Current advances in processing power and memory
allows for the processing of a huge amount of data. BOLD allows for analyzing
policies and the use of these data in models to better predict the effect of new policies.
1.3.2 Rise of Hybrid Simulation Approaches
In policy implementation and execution, many actors are involved and there are a
huge number of factors influencing the outcomes; this complicates the prediction
of the policy outcomes. Simulation models are capable of capturing the interdepen-
dencies between the many factors and can include stochastic elements to deal with
the variations and uncertainties. Simulation is often used in policy-making as an
instrument to gain insight in the impact of possible policies which often result in
new ideas for policies. Simulation allows decision-makers to understand the essence
of a policy, to identify opportunities for change, and to evaluate the effect of pro-
posed changes in key performance indicators (Banks 1998; Law and Kelton 1991).
Simulation heavily depends on data and as such can benefit from big and open data.
Simulation models should capture the essential aspects of reality. Simulation
models do not rely heavily on mathematical abstraction and are therefore suitable
for modeling complex systems (Pidd 1992). Already the development of a model
can raise discussions about what to include and what factors are of influence, in this
way contributing to a better understanding of the situation at hand. Furthermore,
experimentation using models allows one to investigate different settings and the
influence of different scenarios in time on the policy outcomes.
w.jager@rug.nl

1 Introduction to Policy-Making in the Digital Age 7
The effects of policies are hard to predict and dealing with uncertainty is a key
aspect in policy modeling. Statistical representation of real-world uncertainties is
an integral part of simulation models (Law and Kelton 1991). The dynamics asso-
ciated with many factors affecting policy-making, the complexity associated with
the interdependencies between individual parts, and the stochastic elements asso-
ciated with the randomness and unpredictable behavior of transactions complicates
the simulations. Computer simulations for examining, explaining, and predicting so-
cial processes and relationships as well as measuring the possible impact of policies
has become an important part of policy-making. Traditional models are not able to
address all aspects of complex policy interactions, which indicates the need for the
development of hybrid simulation models consisting of a combinatory set of models
built on different modeling theories (Koliba and Zia 2012). In policy-making it can
be that multiple models are developed, but it is also possible to combine various
types of simulation in a single model. For this purpose agent-based modeling and
simulation approaches can be used as these allow for combining different type of
models in a single simulation.
1.3.3 Ubiquitous User Engagement
Efforts to design public policies are confronted with considerable complexity, in
which (1) a large number of potentially relevant factors needs to be considered, (2) a
vast amount of data needs to be processed, (3) a large degree of uncertainty may exist,
and (4) rapidly changing circumstances need to be dealt with. Utilizing computational
methods and various types of simulation and modeling methods is often key to
solving these kinds of problems (Koliba and Zia 2012). The open data and social
media movements are making large quantities of new data available. At the same time
enhancements in computational power have expanded the repertoire of instruments
and tools available for studying dynamic systems and their interdependencies. In
addition, sophisticated techniques for data gathering, visualization, and analysis have
expanded our ability to understand, display, and disseminate complex, temporal, and
spatial information to diverse audiences. These problems can only be addressed from
a complexity science perspective and with a multitude of views and contributions
from different disciplines. Insights and methods of complexity science should be
applied to assist policy-makers as they tackle societal problems in policy areas such
as environmental protection, economics, energy, security, or public safety and health.
This demands user involvement which is supported by visualization techniques and
which can be actively involved by employing (serious) games. These methods can
show what hypothetically will happen when certain policies are implemented.
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8 M. Janssen and M. A. Wimmer
1.4 Combining Disciplines in E-government Policy-Making
This new field has been shaped using various names, including e-policy-making,
digital policy science, computational intelligence, digital sciences, data sciences,
and policy informatics (Dawes and Janssen 2013). The essence of this field it that it
is
1. Practice-driven
2. Employs modeling techniques
3. Needs the knowledge coming from various disciplines
4. It focused on governance and policy-making
This field is practice-driven by taking as a starting point the public policy problem and
defining what information is relevant for addressing the problem under study. This
requires understanding of public administration and policy-making processes. Next,
it is a key to determine how to obtain, store, retrieve, process, model, and interpret the
results. This is the field of e-participation, policy-modeling, social simulation, and
complex systems. Finally, it should be agreed upon how to present and disseminate
the results so that other researchers, decision-makers, and practitioners can use it.
This requires in-depth knowledge of practice, of structures of public administration
and constitutions, political cultures, processes and culture and policy-making.
Based on the ideas, the FP7 project EgovPoliNet project has created an inter-
national community in ICT solutions for governance and policy-modeling. The
“policy-making 2.0” LinkedIn community has a large number of members from dif-
ferent disciplines and backgrounds representing practice and academia. This book
is the product of this project in which a large number of persons from various dis-
ciplines and representing a variety of communities were involved. The book shows
experiences and advances in various areas of policy-making. Furthermore, it contains
comparative analyses and descriptions of cases, tools, and scientific approaches from
the knowledge base created in this project. Using this book, practices and knowl-
edge in this field is shared among researchers. Furthermore, this book provides the
foundations in this area. The covered expertise include a wide range of aspects for so-
cial and professional networking and multidisciplinary constituency building along
the axes of technology, participative processes, governance, policy-modeling, social
simulation, and visualization. In this way eGovPoliNet has advanced the way re-
search, development, and practice is performed worldwide in using ICT solutions
for governance and policy-modeling.
Although in Europe the term “e-government policy” or “e-policy,” for short, is
often used to refer to these types of phenomena, whereas in the USA often the term
“policy informatics” is used. This is similar to that in the USA the term digital
government is often used, whereas in Europe the term e-government is preferred.
Policy informatics is defined as “the study of how information is leveraged and efforts
are coordinated towards solving complex public policy problems” (Krishnamurthy
et al. 2013, p. 367). These authors view policy informatics as an emerging research
space to navigate through the challenges of complex layers of uncertainty within
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1 Introduction to Policy-Making in the Digital Age 9
governance processes. Policy informatics community has created Listserv called
Policy Informatics Network (PIN-L).
E-government policy-making is closely connected to “data science.” Data science
is the ability to find answers from larger volumes of (un)structured data (Davenport
and Patil 2012). Data scientists find and interpret rich data sources, manage large
amounts of data, create visualizations to aid in understanding data, build mathemat-
ical models using the data, present and communicate the data insights/findings to
specialists and scientists in their team, and if required to a nonexpert audience. These
are activities which are at the heart of policy-making.
1.5 Overview of Chapters
In total 54 different authors were involved in the creation of this book. Some chapters
have a single author, but most of the chapters have multiple authors. The authors rep-
resent a wide range of disciplines as shown in Fig. 1.2. The focus has been on targeting
five communities that make up the core field for ICT-enabled policy-making. These
communities include e-government/e-participation, information systems, complex
systems, public administration, and policy research and social simulation. The com-
bination of these disciplines and communities are necessary to tackle policy problems
in new ways. A sixth category was added for authors not belonging to any of these
communities, such as philosophy and economics. Figure 1.3 shows that the authors
are evenly distributed among the communities, although this is less with the chapter.
Most of the authors can be classified as belonging to the e-government/e-participation
community, which is by nature interdisciplinary.
Foundation The first part deals with the foundations of the book. In their Chap. 2
Chris Koliba and Asim Zia start with a best practice to be incorporated in public
administration educational programs to embrace the new developments sketched in
EGOV
IS
Complex Systems
Public Administration and
Policy Research
Social Simulation
other (philosophy, energy,
economics, )
Fig. 1.3 Overview of the disciplinary background of the authors
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10 M. Janssen and M. A. Wimmer
this chapter. They identify two types of public servants that need to be educated.
The policy informatics include the savvy public manager and the policy informatics
analyst. This chapter can be used as a basis to adopt interdisciplinary approaches and
include policy informatics in the public administration curriculum.
Petra Ahrweiler and Nigel Gilbert discuss the need for the quality of simulation
modeling in their Chap. 3. Developing simulation is always based on certain as-
sumptions and a model is as good as the developer makes it. The user community is
proposed to assess the quality of a policy-modeling exercise. Communicative skills,
patience, willingness to compromise on both sides, and motivation to bridge the
formal world of modelers and the narrative world of policy-makers are suggested as
key competences. The authors argue that user involvement is necessary in all stages
of model development.
Wander Jager and Bruce Edmonds argue that due to the complexity that many
social systems are unpredictable by nature in their Chap. 4. They discuss how some
insights and tools from complexity science can be used in policy-making. In particular
they discuss the strengths and weaknesses of agent-based modeling as a way to gain
insight in the complexity and uncertainty of policy-making.
In the Chap. 5, Erik Pruyt sketches the future in which different systems modeling
schools and modeling methods are integrated. He shows that elements from policy
analysis, data science, machine learning, and computer science need to be combined
to deal with the uncertainty in policy-making. He demonstrates the integration of
various modeling and simulation approaches and related disciplines using three cases.
Modeling approaches are compared in the Chap. 6 authored by Dragana Majs-
torovic, Maria A. Wimmer, Roy Lay-Yee, Peter Davis,and Petra Ahrweiler. Like in
the previous chapter they argue that none of the theories on its own is able to address
all aspects of complex policy interactions, and the need for hybrid simulation models
is advocated.
The next chapter is complimentary to the previous chapter and includes a com-
parison of ICT tools and technologies. The Chap. 7 is authored by Eleni Kamateri,
Eleni Panopoulou, Efthimios Tambouris, Konstantinos Tarabanis, Adegboyega Ojo,
Deirdre Lee, and David Price. This chapter can be used as a basis for tool selecting
and includes visualization, argumentation, e-participation, opinion mining, simula-
tion, persuasive, social network analysis, big data analytics, semantics, linked data
tools, and serious games.
Social Aspects, Stakeholders and Values Although much emphasis is put on mod-
eling efforts, the social aspects are key to effective policy-making. The role of values
is discussed in the Chap. 8 authored by Andreas Ligtvoet, Geerten van de Kaa, Theo
Fens, Cees van Beers, Paulien Herder, and Jeroen van den Hoven. Using the case of
the design of smart meters in energy networks they argue that policy-makers would
do well by not only addressing functional requirements but also by taking individual
stakeholder and PVs into consideration.
In policy-making a wide range of stakeholders are involved in various stages
of the policy-making process. Natalie Helbig, Sharon Dawes, Zamira Dzhusupova,
Bram Klievink, and Catherine Gerald Mkude analyze five case studies of stakeholder
w.jager@rug.nl

1 Introduction to Policy-Making in the Digital Age 11
engagement in policy-making in their Chap. 9. Various engagement tools are dis-
cussed and factors identified which support the effective use of particular tools and
technologies.
The Chap. 10 investigates the role of values and trust in computational models in
the policy process. This chapter is authored by Rebecca Moody and Lasse Gerrits. The
authors found that a large diversity exists in values within the cases. By the authors
important explanatory factors were found including (1) the role of the designer of
the model, (2) the number of different actors (3) the level of trust already present,
and (4) and the limited control of decision-makers over the models.
Bureaucratic organizations are often considered to be inefficient and not customer
friendly. Tjeerd Andringa presents and discusses a multidisciplinary framework con-
taining the drivers and causes of bureaucracy in the Chap. 11. He concludes that the
reduction of the number of rules and regulations is important, but that motivating
workers to understand their professional roles and to learn to oversee the impact of
their activities is even more important.
Crowdsourcing has become an important policy instrument to gain access to
expertise (“wisdom”) outside own boundaries. In the Chap. 12, Euripids Loukis
and Yannis Charalabidis discuss Web 2.0 social media for crowdsourcing. Passive
crowdsourcing exploits the content generated by users, whereas active crowdsourcing
stimulates content postings and idea generation by users. Synergy can be created by
combining both approaches. The results of passive crowdsourcing can be used for
guiding active crowdsourcing to avoid asking users for similar types of input.
Policy, Collaboration and Games Agent-based gaming (ABG) is used as a tool
to explore the possibilities to manage complex systems in the Chap. 13 by Wander
Jager and Gerben van der Vegt. ABG allows for modeling a virtual and autonomous
population in a computer game setting to exploit various management and leadership
styles. In this way ABG contribute to the development of the required knowledge on
how to manage social complex behaving systems.
Micro simulation focuses on modeling individual units and the micro-level pro-
cesses that affect their development. The concepts of micro simulation are explained
by Roy Lay-Yee and Gerry Cotterell in the Chap. 14. Micro simulation for pol-
icy development is useful to combine multiple sources of information in a single
contextualized model to answer “what if” questions on complex social phenomena.
Visualization is essential to communicate the model and the results to a variety
of stakeholders. These aspects are discussed in the Chap. 15 by Tobias Ruppert,
Jens Dambruch, Michel Krämer, Tina Balke, Marco Gavanelli, Stefano Bragaglia,
Federico Chesani, Michela Milano, and Jörn Kohlhammer. They argue that despite
the significance to use evidence in policy-making, this is seldom realized. Three
case studies that have been conducted in two European research projects for policy-
modeling are presented. In all the cases access for nonexperts to the computational
models by information visualization technologies was realized.
w.jager@rug.nl

12 M. Janssen and M. A. Wimmer
Applications and Practices Different projects have been initiated to study the best
suitable transition process towards renewable energy. In the Chap. 16 by Dominik
Bär, Maria A. Wimmer, Jozef Glova, Anastasia Papazafeiropoulou,and Laurence
Brooks five of these projects are analyzed and compared. They please for transferring
models from one country to other countries to facilitate learning.
Lyudmila Vidyasova, Andrei Chugunov, and Dmitrii Trutnev present experiences
from Russia in their Chap. 17. They argue that informational, analytical, and fore-
casting activities for the processes of socioeconomic development are an important
element in policy-making. The authors provide a brief overview of the history, the
current state of the implementation of information processing techniques, and prac-
tices for the purpose of public administration in the Russian Federation. Finally, they
provide a range of recommendations to proceed.
Urban policy for sustainability is another important area which is directly linked
to the first chapter in this section. In the Chap. 18, Diego Navarra and Simona Milio
demonstrate a system dynamics model to show how urban policy and governance in
the future can support ICT projects in order to reduce energy usage, rehabilitate the
housing stock, and promote sustainability in the urban environment. This chapter
contains examples of sustainable urban development policies as well as case studies.
In the Chap. 19, Tanko Ahmed discusses the digital divide which is blocking
online participation in policy-making processes. Structuration, institutional and
actor-network theories are used to analyze a case study of political zoning. The
author recommends stronger institutionalization of ICT support and legislation for
enhancing participation in policy-making and bridging the digital divide.
1.6 Conclusions
This book is the first comprehensive book in which the various development and disci-
plines are covered from the policy-making perspective driven by ICT developments.
A wide range of aspects for social and professional networking and multidisciplinary
constituency building along the axes of technology, participative processes, gover-
nance, policy-modeling, social simulation, and visualization are investigated. Policy-
making is a complex process in which many stakeholders are involved. PVs can be
used to guide policy-making efforts and to ensure that the many stakeholders have
an understanding of the societal value that needs to be created. There is an infusion
of technology resulting in changing policy processes and stakeholder involvement.
Technologies like social media provides a means to interact with the public, blogs
can be used to express opinions, big and open data provide input for evidence-based
policy-making, the integration of various types of modeling and simulation tech-
niques (hybrid models) can provide much more insight and reliable outcomes, gam-
ing in which all kind of stakeholders are involved open new ways of innovative policy-
making. In addition trends like the freedom of information, the wisdom of the crowds,
and open collaboration changes the landscape further. The policy-making landscape
is clearly changing and this demands a strong need for interdisciplinary research.
w.jager@rug.nl

1 Introduction to Policy-Making in the Digital Age 13
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w.jager@rug.nl

Chapter 2
Educating Public Managers and Policy Analysts
in an Era of Informatics
Christopher Koliba and Asim Zia
Abstract In this chapter, two ideal types of practitioners who may use or cre-
ate policy informatics projects, programs, or platforms are introduced: the policy
informatics-savvy public manager and the policy informatics analyst. Drawing from
our experiences in teaching an informatics-friendly graduate curriculum, we dis-
cuss the range of learning competencies needed for traditional public managers and
policy informatics-oriented analysts to thrive in an era of informatics. The chapter
begins by describing the two different types of students who are, or can be touched
by, policy informatics-friendly competencies, skills, and attitudes. Competencies
ranging from those who may be users of policy informatics and sponsors of policy
informatics projects and programs to those analysts designing and executing policy
informatics projects and programs will be addressed. The chapter concludes with
an illustration of how one Master of Public Administration (MPA) program with a
policy informatics-friendly mission, a core curriculum that touches on policy infor-
matics applications, and a series of program electives that allows students to develop
analysis and modeling skills, designates its informatics-oriented competencies.
2.1 Introduction
The range of policy informatics opportunities highlighted in this volume will require
future generations of public managers and policy analysts to adapt to the oppor-
tunities and challenges posed by big data and increasing computational modeling
capacities afforded by the rapid growth in information technologies. It will be up
to the field’s Master of Public Administration (MPA) and Master of Public Policy
(MPP) programs to provide this next generation with the tools needed to harness the
wealth of data, information, and knowledge increasingly at the disposal of public
C. Koliba (�)
University of Vermont, 103 Morrill Hall, 05405 Burlington, VT, USA
e-mail: ckoliba@uvm.edu
A. Zia
University of Vermont, 205 Morrill Hall, 05405 Burlington, VT, USA
e-mail: azia@uvm.edu
© Springer International Publishing Switzerland 2015 15
M. Janssen et al. (eds.), Policy Practice and Digital Science,
Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_2
w.jager@rug.nl

16 C. Koliba and A. Zia
administrators and policy analysts. In this chapter, we discuss the role of policy infor-
matics in the development of present and future public managers and policy analysts.
Drawing from our experiences in teaching an informatics-friendly graduate curricu-
lum, we discuss the range of learning competencies needed for traditional public
managers and policy informatics-oriented analysts to thrive in an era of informatics.
The chapter begins by describing the two different types of students who are, or can
be touched by, policy informatics-friendly competencies, skills, and attitudes. Com-
petencies ranging from those who may be users of policy informatics and sponsors of
policy informatics projects and programs to those analysts designing and executing
policy informatics projects and programs will be addressed. The chapter concludes
with an illustration of how one MPA program with a policy informatics-friendly
mission, a core curriculum that touches on policy informatics applications, and a
series of program electives that allows students to develop analysis and modeling
skills, designates its informatics-oriented competencies.
2.2 Two Types of Practitioner Orientations to Policy Informatics
Drawn from our experience, we find that there are two “ideal types” of policy infor-
matics practitioner, each requiring greater and greater levels of technical mastery of
analytics techniques and approaches. These ideal types are: policy informatics-savvy
public managers and policy informatics analysts.
A policy informatics-savvy public manager may take on one of two possible roles
relative to policy informatics projects, programs, or platforms. They may play instru-
mental roles in catalyzing and implementing informatics initiatives on behalf of their
organizations, agencies, or institutions. In the manner, they may work with technical
experts (analysts) to envision possible uses for data, visualizations, simulations, and
the like. Public managers may also be in the role of using policy informatics projects,
programs, or platforms. They may be in positions to use these initiatives to ground
decision making, allocate resources, and otherwise guide the performance of their
organizations.
A policy informatics analyst is a person who is positioned to actually execute
a policy informatics initiative. They may be referred to as analysts, researchers,
modelers, or programmers and provide the technical assistance needed to analyze
databases, build and run models, simulations, and otherwise construct useful and
effective policy informatics projects, programs, or platforms.
To succeed in either and both roles, managers and analysts will require a certain set
of skills, knowledge, or competencies. Drawing on some of the prevailing literature
and our own experiences, we lay out an initial list of potential competencies for
consideration.
w.jager@rug.nl

2 Educating Public Managers and Policy Analysts in an Era of Informatics 17
2.2.1 Policy Informatics-Savvy Public Managers
To successfully harness policy informatics, public managers will likely not need to
know how to explicitly build models or manipulate big data. Instead, they will need
to know what kinds of questions that policy informatics projects or programs can
answer or not answer. They will need to know how to contract with and/or manage
data managers, policy analysts, and modelers. They will need to be savvy consumers
of data analysis and computational models, but not necessarily need to know how to
technically execute them. Policy informatics projects, programs, and platforms are
designed and executed in some ways, as any large-scale, complex project.
In writing about the stages of informatics project development using “big data,”
DeSouza lays out project development along three stages: planning, execution, and
postimplementation. Throughout the project life cycle, he emphasizes the role of
understanding the prevailing policy and legal environment, the need to venture into
coalition building, the importance of communicating the broader opportunities af-
forded by the project, the need to develop performance indicators, and the importance
of lining up adequate financial and human resources (2014).
Framing what traditional public managers need to know and do to effectively
interface with policy informatics projects and programs requires an ability to be a
“systems thinker,” an effective evaluator, a capacity to integrate informatics into
performance and financial management systems, effective communication skills,
and a capacity to draw on social media, information technology, and e-governance
approaches to achieve common objectives. We briefly review each of these capacities
below.
Systems Thinking Knowing the right kinds of questions that may be asked through
policy informatics projects and programs requires public managers to possess a “sys-
tems” view. Much has been written about the importance of “systems thinking” for
public managers (Katz and Kahn 1978; Stacey 2001; Senge 1990; Korton 2001).
Taking a systems perspective allows public managers to understand the relationship
between the “whole” and the “parts.” Systems-oriented public managers will possess
a level of situational awareness (Endsley 1995) that allows them to see and under-
stand patterns of interaction and anticipate future events and orientations. Situational
awareness allows public mangers to understand and evaluate where data are coming
from, how best data are interpreted, and the kinds of assumptions being used in
specific interpretations (Koliba et al. 2011). The concept of system thinking laid out
here can be associated with the notion of transition management (Loorbach 2007).
Process Orientations to Public Policy The capacity to view the policy making and
implementation process as a process that involves certain levels of coordination
and conflict between policy actors is of critical importance for policy informatics-
savvy public managers and analysts. Understanding how data are used to frame
problems and policy solutions, how complex governance arrangements impact policy
implementation (Koliba et al. 2010), and how data visualization can be used to
w.jager@rug.nl

18 C. Koliba and A. Zia
facilitate the setting of policy agendas and open policy windows (Kingdon 1984) is
of critical importance for public management and policy analysts alike.
Research Methodologies Another basic competency needed for any public manager
using policy informatics is a foundational understanding of research methods, par-
ticularly quantitative reasoning and methodologies. A foundational understanding of
data validity, analytical rigor and relevance, statistical significance, and the like are
needed to be effective consumers of informatics. That said, traditional public man-
agers should also be exposed to qualitative methods as well, refining their powers of
observation, understanding how symbols, stories, and numbers are used to govern,
and how data and data visualization and computer simulations play into these mental
models.
Performance Management A key feature of systems thinking as applied to policy
informatics is the importance of understanding how data and analysis are to be
used and who the intended users of the data are (Patton 2008). The integration of
policy informatics into strategic planning (Bryson 2011), performance management
systems (Moynihan 2008), and ultimately woven into an organization’s capacity to
learn, adapt, and evolve (Argyis and Schön 1996) are critically important in this
vein. As policy informatics trends evolve, public managers will likely need to be
exposed to uses of decision support tools, dashboards, and other computationally
driven models and visualizations to support organizational performance.
Financial Management Since the first systemic budgeting systems were put in place,
public managers have been urged to use the budgeting process as a planning and eval-
uation tool (Willoughby 1918). This approach was formally codified in the 1960s
with the planning–programming–budgeting (PPB) system with its focus on plan-
ning, managerial, and operational control (Schick 1966) and later adopted into more
contemporary approaches to budgeting (Caiden 1981). Using informative projects,
programs, or platforms to make strategic resource allocation decisions is a necessary
given and a capacity that effective public managers must master. Likewise, the pol-
icy analyst will likely need to integrate financial resource flows and costs into their
projects.
Collaborative and Cooperative Capacity Building The development and use of pol-
icy informatics projects, programs, or platforms is rarely, if ever, undertaken as
an individual, isolated endeavor. It is more likely that such initiatives will require
interagency, interorganizational, or intergroup coordination. It is also likely that
content experts will need to be partnered with analysts and programmers to com-
plete tasks and execute designs. The public manager and policy analyst must both
possess the capacity to facilitate collaborative management functions (O’Leary and
Bingham 2009).
Basic Communication Skills This perhaps goes without saying, but the heart of any
informatics project lies in the ability to effectively communicate findings and ideas
through the analysis of data.
w.jager@rug.nl

2 Educating Public Managers and Policy Analysts in an Era of Informatics 19
Social Media, Information Technology, and e-Governance Awareness A final com-
petency concerns public managers’ capacity to deepen their understanding of how
social media, Web-based tools, and related information technologies are being em-
ployed to foster various e-government, e-governance, and related initiatives (Mergel
2013). Placing policy informatics projects and programs within the context of these
larger trends and uses is something that public managers must be exposed to.
Within our MPA program, we have operationalized these capacities within a four-
point rubric that outlines what a student needs to do to demonstrate meeting these
standards. The rubric below highlights 8 of our program’s 18 capacities. All 18 of
these capacities are situated under 1 of the 5 core competencies tied to the accred-
itation standards of the Network of Schools of Public Affairs and Administration
(NASPAA), the professional accrediting association in the USA, and increasingly in
other countries as well, for MPA and MPP programs. A complete list of these core
competencies and the 18 capacities nested under them are provided in Appendix of
this chapter.
The eight capacities that we have singled out as being the most salient to the role
of policy informatics in public administration are provided in Table 2.1. The rubric
follows a four-point scale, ranging from “does not meet standard,” “approaches
standard,” “meets standard,” and “exceeds standard.”
2.2.2 Policy Informatics Analysts
A second type of practitioner to be considered is what we are referring to as a “policy
informatics analyst.” When considering the kinds of competencies that policy infor-
matics analysts need to be successful, we first assume that the basic competencies
outlined in the prior section apply here as well. In other words, effective policy in-
formatics analysts must be systems thinkers in order to place data and their analysis
into context, be cognizant of current uses of decision support systems (and related
platforms) to enable organizational learning, performance, and strategic planning,
and possess an awareness of e-governance and e-government initiatives and how they
are transforming contemporary public management and policy planning practices.
In addition, policy analysts must possess a capacity to understand policy systems:
How policies are made and implemented? This baseline understanding can then be
used to consider the placement, purpose, and design of policy informatics projects
or programs. We lay out more specific analyst capacities below.
Advanced Research Methods of Information Technology Applications In many in-
stances, policy informatics analysts will need to move beyond meeting the standard.
This is particularly true in the area of exceeding the public manager standards for re-
search methods and utilization of information technology. It is assumed that effective
policy informatics analysts will have a strong foundation in quantitative methodolo-
gies and applications. To obtain these skills, policy analysts will need to move beyond
basic surveys of research methods into more advanced research methods curriculum.
w.jager@rug.nl

20 C. Koliba and A. Zia
T
ab
le
2.
1
P
ub
li
c
m
an
ag
er
po
li
cy
in
fo
rm
at
ic
s
ca
pa
ci
ti
es
C
ap
ac
it
y
D
oe
s
no
t
m
ee
t
st
an
da
rd
A
pp
ro
ac
he
s
st
an
da
rd
M
ee
ts
st
an
da
rd
E
xc
ee
ds
st
an
da
rd
C
a
p
a
ci
ty
to
a
p
p
ly
kn
o
w
le
d
ge
o
f
sy
st
em
d
yn
a
m
ic
s
a
n
d
n
et
w
o
rk
st
ru
ct
u
re
s
in
p
u
b
li
c
a
d
m
in
is
tr
a
ti
o
n
p
ra
ct
ic
es
D
oe
s
no
t
un
de
rs
ta
nd
th
e
ba
si
c
op
er
at
io
ns
of
sy
st
em
s
an
d
ne
tw
or
ks
;
ca
nn
ot
ex
pl
ai
n
w
hy
un
de
rs
ta
nd
in
g
ca
se
s
an
d
co
nt
ex
ts
in
te
rm
s
of
sy
st
em
s
an
d
ne
tw
or
ks
is
im
po
rt
an
t
C
an
pr
ov
id
e
a
ba
si
c
ov
er
vi
ew
of
w
ha
t
sy
st
em
dy
na
m
ic
s
an
d
ne
tw
or
k
st
ru
ct
ur
es
ar
e
an
d
il
lu
st
ra
te
ho
w
th
ey
ar
e
ev
id
en
t
in
pa
rt
ic
ul
ar
ca
se
s
an
d
co
nt
ex
ts
Is
ab
le
to
un
de
rt
ak
e
an
an
al
ys
is
of
a
co
m
pl
ex
pu
bl
ic
ad
m
in
is
tr
at
io
n
is
su
e,
pr
ob
le
m
,
or
co
nt
ex
t
us
in
g
ba
si
c
sy
st
em
dy
na
m
ic
s
an
d
ne
tw
or
k
fr
am
ew
or
ks
C
an
ap
pl
y
sy
st
em
dy
na
m
ic
s
an
d
ne
tw
or
k
fr
am
ew
or
ks
to
ex
is
ti
ng
ca
se
s
an
d
co
nt
ex
ts
to
de
ri
ve
w
or
ki
ng
so
lu
ti
on
s
or
fe
as
ib
le
al
te
rn
at
iv
es
to
pr
es
si
ng
ad
m
in
is
tr
at
iv
e
an
d
po
li
cy
pr
ob
le
m
s
C
a
p
a
ci
ty
to
a
p
p
ly
p
o
li
cy
st
re
a
m
s,
cy
cl
es
,
sy
st
em
s
fo
ci
u
p
o
n
p
a
st
,
p
re
se
n
t,
a
n
d
fu
tu
re
p
o
li
cy
is
su
es
,
a
n
d
to
u
n
d
er
st
a
n
d
h
o
w
p
ro
b
le
m
id
en
ti
fi
ca
ti
o
n
im
p
a
ct
s
p
u
b
li
c
a
d
m
in
is
tr
a
ti
o
n
P
os
se
ss
es
li
m
it
ed
ca
pa
ci
ty
to
ut
il
iz
e
po
li
cy
st
re
am
s
an
d
po
li
cy
st
ag
e
he
ur
is
ti
cs
m
od
el
to
de
sc
ri
be
ob
se
rv
ed
ph
en
om
en
a.
C
an
is
ol
at
e
si
m
pl
e
pr
ob
le
m
s
fr
om
so
lu
ti
on
s,
bu
t
ha
s
di
ffi
cu
lt
ly
se
pa
ra
ti
ng
il
l-
st
ru
ct
ur
ed
pr
ob
le
m
s
fr
om
so
lu
ti
on
s
P
os
se
ss
es
so
m
e
ca
pa
ci
ty
to
ut
il
iz
e
po
li
cy
st
re
am
s
an
d
to
de
sc
ri
be
po
li
cy
st
ag
e
he
ur
is
ti
cs
m
od
el
ob
se
rv
ed
ph
en
om
en
a.
P
os
se
ss
es
so
m
e
ca
pa
ci
ty
to
de
fi
ne
ho
w
pr
ob
le
m
s
ar
e
fr
am
ed
by
di
ff
er
en
t
po
li
cy
ac
to
rs
E
m
pl
oy
s
a
po
li
cy
st
re
am
s
or
po
li
cy
st
ag
e
he
ur
is
ti
cs
m
od
el
ap
pr
oa
ch
to
th
e
st
ud
y
of
ob
se
rv
ed
ph
en
om
en
a.
C
an
de
m
on
st
ra
te
ho
w
pr
ob
le
m
de
fi
ni
ti
on
is
de
fi
ne
d
w
it
hi
n
sp
ec
ifi
c
po
li
cy
co
nt
ex
ts
an
d
de
co
ns
tr
uc
t
th
e
re
la
ti
on
sh
ip
be
tw
ee
n
pr
ob
le
m
de
fi
ni
ti
on
s
an
d
so
lu
ti
on
s
E
m
pl
oy
s
a
po
li
cy
st
re
am
s
or
po
li
cy
st
ag
e
he
ur
is
ti
cs
m
od
el
ap
pr
oa
ch
to
th
e
di
ag
no
si
s
of
a
pr
ob
le
m
ra
is
ed
in
re
al
-l
if
e
po
li
cy
di
le
m
m
as
.C
an
ar
ti
cu
la
te
ho
w
co
nfl
ic
ts
ov
er
pr
ob
le
m
de
fi
ni
ti
on
co
nt
ri
bu
te
to
w
ic
ke
d
po
li
cy
pr
ob
le
m
s
C
a
p
a
ci
ty
to
em
p
lo
y
q
u
a
n
ti
ta
ti
ve
a
n
d
q
u
a
li
ta
ti
ve
re
se
a
rc
h
m
et
h
o
d
s
fo
r
p
ro
g
ra
m
ev
a
lu
a
ti
o
n
a
n
d
a
ct
io
n
re
se
a
rc
h
P
os
se
ss
es
a
li
m
it
ed
ca
pa
ci
ty
to
em
pl
oy
su
rv
ey
,i
nt
er
vi
ew
,o
r
ot
he
r
so
ci
al
re
se
ar
ch
m
et
ho
ds
to
a
fo
cu
s
ar
ea
.C
an
ex
pl
ai
n
w
hy
it
is
im
po
rt
an
t
to
un
de
rt
ak
e
pr
og
ra
m
or
pr
oj
ec
t
ev
al
ua
ti
on
,b
ut
po
ss
es
se
s
li
m
it
ed
ca
pa
ci
ty
to
ac
tu
al
ly
ca
rr
yi
ng
it
ou
t
D
em
on
st
ra
te
s
a
ca
pa
ci
ty
to
em
pl
oy
su
rv
ey
,i
nt
er
vi
ew
,o
r
ot
he
r
so
ci
al
re
se
ar
ch
m
et
ho
ds
to
a
fo
cu
s
ar
ea
an
d
an
un
de
rs
ta
nd
in
g
of
ho
w
su
ch
da
ta
an
d
an
al
ys
is
ar
e
us
ef
ul
in
ad
m
in
is
tr
at
iv
e
pr
ac
ti
ce
.C
an
pr
ov
id
e
a
ra
ti
on
al
e
fo
r
un
de
rt
ak
in
g
pr
og
ra
m
/p
ro
je
ct
C
an
pr
ov
id
e
a
pi
ec
e
of
or
ig
in
al
an
al
ys
is
of
an
ob
se
rv
ed
ph
en
om
en
on
em
pl
oy
in
g
on
e
qu
al
it
at
iv
e
or
qu
an
ti
ta
ti
ve
m
et
ho
do
lo
gy
ef
fe
ct
iv
el
y.
P
os
se
ss
es
ca
pa
ci
ty
to
co
m
m
is
si
on
a
pi
ec
e
of
or
ig
in
al
re
se
ar
ch
.C
an
pr
ov
id
e
a
de
ta
il
ed
ac
co
un
t
fo
r
ho
w
a
D
em
on
st
ra
te
s
th
e
ca
pa
ci
ty
to
un
de
rt
ak
e
an
in
de
pe
nd
en
t
re
se
ar
ch
ag
en
da
th
ro
ug
h
em
pl
oy
in
g
on
e
or
m
or
e
so
ci
al
re
se
ar
ch
m
et
ho
ds
ar
ou
nd
a
to
pi
c
of
st
ud
y
of
im
po
rt
an
ce
to
pu
bl
ic
ad
m
in
is
tr
at
io
n.
C
an
de
m
on
st
ra
te
th
e
su
cc
es
sf
ul
ex
ec
ut
io
n
of
a
pr
og
ra
m
or
w.jager@rug.nl

2 Educating Public Managers and Policy Analysts in an Era of Informatics 21
T
ab
le
2.
1
(c
on
ti
nu
ed
)
C
ap
ac
it
y
D
oe
s
no
t
m
ee
t
st
an
da
rd
A
pp
ro
ac
he
s
st
an
da
rd
M
ee
ts
st
an
da
rd
E
xc
ee
ds
st
an
da
rd
ev
al
ua
ti
on
an
d
ex
pl
ai
n
w
ha
t
th
e
po
ss
ib
le
go
al
s
an
d
ou
tc
om
es
of
su
ch
an
ev
al
ua
ti
on
m
ig
ht
be
pr
og
ra
m
or
pr
oj
ec
t
ev
al
ua
ti
on
pr
oj
ec
t
sh
ou
ld
be
st
ru
ct
ur
ed
w
it
hi
n
th
e
co
nt
ex
t
of
a
sp
ec
ifi
c
pr
og
ra
m
or
pr
oj
ec
t
pr
oj
ec
t
ev
al
ua
ti
on
or
th
e
su
cc
es
sf
ul
ut
il
iz
at
io
n
of
a
pr
og
ra
m
or
pr
oj
ec
t
ev
al
ua
ti
on
to
im
pr
ov
e
ad
m
in
is
tr
at
iv
e
pr
ac
ti
ce
C
a
p
a
ci
ty
to
a
p
p
ly
so
u
n
d
p
er
fo
rm
a
n
ce
m
ea
su
re
m
en
t
a
n
d
m
a
n
ag
em
en
t
p
ra
ct
ic
es
C
an
pr
ov
id
e
an
ex
pl
an
at
io
n
of
w
hy
pe
rf
or
m
an
ce
go
al
s
an
d
m
ea
su
re
s
ar
e
im
po
rt
an
t
in
pu
bl
ic
ad
m
in
is
tr
at
io
n,
bu
t
ca
nn
ot
ap
pl
y
th
is
re
as
on
in
g
to
sp
ec
ifi
c
co
nt
ex
ts
C
an
id
en
ti
fy
th
e
pe
rf
or
m
an
ce
m
an
ag
em
en
t
co
ns
id
er
at
io
ns
fo
r
a
pa
rt
ic
ul
ar
si
tu
at
io
n
or
co
nt
ex
t,
bu
t
ha
s
li
m
it
ed
ca
pa
ci
ty
to
ev
al
ua
te
th
e
ef
fe
ct
iv
en
es
s
of
pe
rf
or
m
an
ce
m
an
ag
em
en
t
sy
st
em
s
C
an
id
en
ti
fy
an
d
an
al
yz
e
pe
rf
or
m
an
ce
m
an
ag
em
en
t
sy
st
em
s,
ne
ed
s,
an
d
em
er
gi
ng
op
po
rt
un
it
ie
s
w
it
hi
n
a
sp
ec
ifi
c
or
ga
ni
za
ti
on
or
ne
tw
or
k
C
an
pr
ov
id
e
ne
w
in
si
gh
ts
in
to
th
e
pe
rf
or
m
an
ce
m
an
ag
em
en
t
ch
al
le
ng
es
fa
ci
ng
an
or
ga
ni
za
ti
on
or
ne
tw
or
k,
an
d
su
gg
es
t
al
te
rn
at
iv
e
de
si
gn
an
d
m
ea
su
re
m
en
t
sc
en
ar
io
s
C
a
p
a
ci
ty
to
a
p
p
ly
so
u
n
d
fi
n
a
n
ci
a
l
p
la
n
n
in
g
a
n
d
fi
sc
a
l
re
sp
o
n
si
b
il
it
y
C
an
id
en
ti
fy
w
hy
bu
dg
et
in
g
an
d
so
un
d
fi
sc
al
m
an
ag
em
en
t
pr
ac
ti
ce
s
ar
e
im
po
rt
an
t,
bu
t
ca
nn
ot
an
al
yz
e
ho
w
an
d/
or
if
su
ch
pr
ac
ti
ce
s
ar
e
be
in
g
us
ed
w
it
hi
n
sp
ec
ifi
c
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ll
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co
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en
t
w.jager@rug.nl

22 C. Koliba and A. Zia
T
ab
le
2.
1
(c
on
ti
nu
ed
)
C
ap
ac
it
y
D
oe
s
no
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m
ee
t
st
an
da
rd
A
pp
ro
ac
he
s
st
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da
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ee
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da
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xc
ee
ds
st
an
da
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p
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ta
ke
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ig
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a
li
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o
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m
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u
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ic
a
ti
o
n
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em
on
st
ra
te
s
so
m
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ab
il
it
y
to
ex
pr
es
s
id
ea
s
ve
rb
al
ly
an
d
in
w
ri
ti
ng
.L
ac
ks
co
ns
is
te
nt
ca
pa
ci
ty
to
pr
es
en
t
an
d
w
ri
te
P
os
se
ss
es
th
e
ca
pa
ci
ty
to
w
ri
te
do
cu
m
en
ts
th
at
ar
e
fr
ee
of
gr
am
m
at
ic
al
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ro
rs
an
d
ar
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or
ga
ni
ze
d
in
a
cl
ea
r
an
d
ef
fi
ci
en
t
m
an
ne
r.
P
os
se
ss
es
th
e
ca
pa
ci
ty
to
pr
es
en
t
id
ea
s
in
a
pr
of
es
si
on
al
m
an
ne
r.
S
uf
fe
rs
fr
om
a
la
ck
of
co
ns
is
te
nc
y
in
th
e
pr
es
en
ta
ti
on
of
m
at
er
ia
l
an
d
ex
pr
es
si
on
or
or
ig
in
al
id
ea
s
an
d
co
nc
ep
ts
Is
ca
pa
bl
e
of
co
ns
is
te
nt
ly
ex
pr
es
si
ng
id
ea
s
ve
rb
al
ly
an
d
in
w
ri
ti
ng
in
a
pr
of
es
si
on
al
m
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ne
r
th
at
co
m
m
un
ic
at
es
m
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sa
ge
s
to
in
te
nd
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au
di
en
ce
s
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de
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on
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so
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e
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st
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s
in
w
hi
ch
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rb
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co
m
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un
ic
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s
pe
rs
ua
de
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ot
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to
ta
ke
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ti
on
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a
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n
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ig
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a
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ce
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je
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ex
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w
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fo
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.P
os
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pe
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,b
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de
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ta
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fo
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pr
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a
pu
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at
or
su
cc
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ul
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to
us
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to
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rv
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im
pa
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s
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s
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.
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an
di
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m
s
as
so
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w
it
h
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to
ol
s,
pr
oc
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,a
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em
on
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ra
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s
a
ca
pa
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to
vi
ew
IT
in
te
rm
s
of
sy
st
em
s
de
si
gn
.I
s
ca
pa
bl
e
of
w
or
ki
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w
it
h
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pr
of
es
si
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s
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id
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ti
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of
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r
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up
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ad
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,I
T
pr
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ur
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us
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re
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tt
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g
IT
in
fo
rm
at
io
n
te
ch
no
lo
gy
w.jager@rug.nl

2 Educating Public Managers and Policy Analysts in an Era of Informatics 23
Competencies in advanced quantitative methods in which students learn to clean and
manage large databases, perform advanced statistical tests, develop linear regression
models to describe causal relationship, and the like are needed. Capacity to work
across software platforms such as Excel, Statistical Package for the Social Sciences
(SPSS), Analytica, and the like are important. Increasingly, the capacity to triangu-
late different methods, including qualitative approaches such as interviews, focus
groups, participant observations is needed.
Data Visualization and Design Not only must analysts be aware of how these meth-
ods and decision support platforms may be used by practitioners but also they must
know how to design and implement them. Therefore, we suggest that policy infor-
matics analysts be exposed to design principles and how they may be applied to
decision support systems, big data projects, and the like. Policy informatics analysts
will need to understand and appreciate how data visualization techniques are being
employed to “tell a story” through data.
Figure 2.1 provides an illustration of one student’s effort to visualize campaign
donations to state legislatures from the gas-extraction (fracking) industry undertaken
by a masters student, Jeffery Castle for a system analysis and strategic management
class taught by Koliba.
Castle’s project demonstrates the power of data visualization to convey a central
message drawing from existing databases. With a solid research methods background
and exposure to visualization and design principles in class, he was able to develop
an insightful policy informatics project.
Basic to Advanced Programming Language Skills Arguably, policy informatics ana-
lysts will possess a capacity to visualize and present data in a manner that is accessible.
Increasingly, web-based tools are being used to design user interfaces. Knowledge
of JAVA and HTML are likely most helpful in these regards. In some instances,
original programs and models will need to be written through the use of program-
ming languages such as Python, R, C++, etc. The extent to which existing software
programs, be they open source or proprietary, provide enough utility to execute pol-
icy informatics projects, programs, or platforms is a continuing subject of debate
within the policy informatics community. Exactly how much and to what extent spe-
cific programming languages and software programs are needing to be mastered is
a standing question. For the purposes of writing this chapter, we rely on our current
baseline observations and encourage more discussion and debate about the range of
competencies needed by successful policy analysts.
Basic to More Advanced Modeling Skills More advanced policy informatics analysts
will employ computational modeling approaches that allow for the incorporation of
more complex interactions between variables. These models may be used to capture
systems as dynamic, emergent, and path dependent. The outputs of these models
may allow for scenario testing through simulation (Koliba et al. 2011). With the
advancement of modeling software, it is becoming easier for analysts to develop
system dynamics models, agent-based models, and dynamic networks designed to
simulate the features of complex adaptive systems. In addition, the ability to manage
and store data and link or wrap databases is often necessary.
w.jager@rug.nl

24 C. Koliba and A. Zia
Fig. 2.1 Campaign contributions to the Pennsylvania State Senate and party membership. The
goal of this analysis is to develop a visualization tool to translate publically available campaign
contribution information into an easily accessible, visually appealing, and interactive format. While
campaign contribution data are filed and available to the public through the Pennsylvania Department
of State, it is not easily synthesized. This analysis uses a publically available database that has been
published on marcellusmoney.org. In order to visualize the data, a tool was used that allows for
the creation of a Sankey diagram that is able to be manipulated and interacted within an Internet
browser. A Sankey diagram visualizes the magnitude of flow between the nodes of a network (Castle
2014)
The ability of analysts to draw on a diverse array of methods and theoretical
frameworks to envision and create models is of critical importance. Any potential
policy informatics project, program, or platform will be enabled or constrained by the
modeling logic in place. With a plurality of tools at one’s disposal, policy informatics
analysts will be better positioned to design relevant and legitimate models.
w.jager@rug.nl

2 Educating Public Managers and Policy Analysts in an Era of Informatics 25
Fig. 2.2 End-stage renal disease (ESRD) system dynamics population model. To provide clinicians
and health care administrators with a greater understanding of the combined costs associated with
the many critical care pathways associated with ESRD, a system dynamics model was designed to
simulate the total expenses of ESRD treatment for the USA, as well as incidence and mortality rates
associated with different critical care pathways: kidney transplant, hemodialysis, peritoneal dialysis,
and conservative care. Calibrated to US Renal Data System (USRDS) 2013 Annual and Historical
Data Report and the US Census Bureau for the years 2005–2010, encompassing all ESRD patients
under treatment in the USA from 2005 to 2010, the ESRD population model predicts the growth and
costs of ESRD treatment type populations using historical patterns. The model has been calibrated
against the output of the USRDS’s own prediction for the year 2020 and also tested by running his-
toric scenarios and comparing the output to existing data. Using a web interface designed to allow
users to alter certain combinations of parameters, several scenarios are run to project future spending,
incidence, and mortalities if certain combinations of critical care pathways are pursued. These sce-
narios include: a doubling of kidney donations and transplant rates, a marked increase in the offering
of peritoneal dialysis, and an increase in conservative care routes for patients over 65. The results
of these scenario runs are shared, demonstrating sizable cost savings and increased survival rates.
Implications of clinical practice, public policy, and further research are drawn (Fernandez 2013)
Figure 2.2 provides an illustration of Luca Fernandez’s system dynamics model of
critical care pathways for end-stage renal disease (ESRD). Fernandez took Koliba’s
system analysis and strategic management course and Zia’s decision-making model-
ing course. This model, constructed using the proprietary software, AnyLogic, was
initially constructed as a project in Zia’s course.
Castle and Fernandez’s projects illustrate how master’s-level students with an
eye toward becoming policy informatics analysts can build skills and capacities to
develop useful informatics projects that can guide policy and public management.
They were guided to this point by taking advanced courses designed explicitly with
policy informatics outcomes in mind.
w.jager@rug.nl

26 C. Koliba and A. Zia
Policy Informatics Analyst
Informatics-Savvy Public
•Advanced research methods •Data visualization and design techniques •Basic to advanced modeling software skills •Basic to advanced programming language(s)
•Systems thinking •Basic understanding of research methods •Knowledge of how to integrate informatics within performance management •Knowledge of how to integrate inofrmatics within financial systems•Effecive written communication •Effective usese of social media / e-governance approaches
Fig. 2.3 The nested capacities of informatics-savvy public managers and policy informatics analysts
Figure 2.3 illustrates how the competencies of the two different ideal types of
policy informatics practitioners are nested inside of one another. A more complete
list of competencies that are needed for the more advanced forms of policy analy-
sis will need to emerge through robust exchanges between the computer sciences,
organizational sciences, and policy sciences. These views will likely hinge on as-
sumptions about the sophistication of the models to be developed. A key question
here concerning the types of models to be built is: Can adequate models be built
using existing software or is original programming needed or desired? Ideally, ad-
vanced policy analysts undertaking policy informatics projects are “programmers
with a public service motivation.”
2.3 Applications to Professional Masters Programs
Professional graduate degree programs have steadily moved toward emphasizing the
importance of the mission of particular graduate programs in determining the optimal
curriculum to suit the learning needs of it students. As a result, clear definitions of
the learning outcomes and the learning needs of particular student communities are
defined. Some programs may seek to serve regional or local needs of the government
and nonprofit sector, while others may have a broader reach, preparing students to
work within federal or international level governments and nonprofits.
In addition to geographic scope, accredited MPA and MPP programs may have
specific areas of concentration. Some programs may focus on preparing public man-
agers who are charged with managing resources, making operational, tactical, and
w.jager@rug.nl

2 Educating Public Managers and Policy Analysts in an Era of Informatics 27
strategic decisions and, overall, administering to the day-to-day needs of a govern-
ment or nonprofit organization. Programs may also focus on training policy analysts
who are responsible for analyzing policies, policy alternatives, problem definition,
and the like. Historically, the differences between public management and policy
analysis have distinguished the MPA degree from the MPP degree. However, recent
studies of NASPAA-accredited programs have found that the lines between MPA and
MPP programs are increasingly blurred (Hur and Hackbart 2009). The relationship
between public management and policy analysis matters to those interested in policy
informatics because these distinctions drive what policy informatics competencies
and capacities are covered within a core curriculum, and what competencies and
capacities are covered within a suite of electives or concentrations.
Competency-based assessments are increasingly being used to evaluate and de-
sign curriculum. Drawing on the core tenants of adult learning theory and practice,
competency-based assessment involves the derivation of specific skills, knowledge,
or attitudes that an adult learner must obtain in order to successfully complete a
course of study or degree requirement. Effective competency-based graduate pro-
grams call on students to demonstrate a mastery of competencies through a variety
of means. Portfolio development, test taking, and project completion are common
applications. Best practices in competency-based education assert that curriculum
be aligned with specific competencies as much as possible.
By way of example, the University of Vermont’s MPA Program has had a “systems
thinking” focus since it was first conceived in the middle 1980s. Within the last 10
years, the two chapter coauthors, along with several core faculty who have been
associated with the program since its inception, have undertaken an effort to refine
its mission based on its original systems-focused orientation.
As of 2010, the program mission was refined to read:
Our MPA program is a professional interdisciplinary degree that prepares pre and in-service
leaders, managers and policy analysts by combining the theoretical and practical founda-
tions of public administration focusing on the complexity of governance systems and the
democratic, collaborative traditions that are a hallmark of Vermont communities.
The mission was revised to include leaders and managers, as well as policy analysts.
A theory-practice link was made explicit. The phrase, “complexity of governance
systems” was selected to align with a commonly shared view of contemporary gover-
nance as a multisectoral and multijurisdictional context. Concepts such as bounded
rationality, social complexity, the importance of systems feedback, and path de-
pendency are stressed throughout the curriculum. The sense of place found within
the State of Vermont was also recognized and used to highlight the high levels of
engagement found within the program.
The capacities laid out in Table 2.1 have been mapped to the program’s core
curriculum. The program’s current core is a set of five courses: PA 301: Foundations
of Public Administration; PA 302: Organizational Behavior and Change; PA 303:
Research Methods; PA 305: Public and Nonprofit Budgeting and Finance and PA
306: Policy Systems. In addition, all students are required to undertake a three-
credit internship and a three-credit Capstone experience in which they construct a
w.jager@rug.nl

28 C. Koliba and A. Zia
final learning portfolio. It is within this final portfolio that students are expected to
provide evidence of meeting or exceeding the standard. An expanded rubric of all
18 capacities is used by the students to undertake their own self-assessment. These
assessments are judged against the Capstone instructor’s evaluation.
In 2009, the MPA faculty revised the core curriculum to align with the core
competencies. Several course titles and content were revised to align with these
competencies and the overall systems’ focus of our mission. The two core courses
taught by the two coauthors, PA 301 and PA 306, are highlighted here.
2.4 PA 301: Foundations of Public Administration
Designed as a survey of the prevailing public administration literature during the past
200 plus years, Foundations of Public Administration is arranged across a continuum
of interconnected themes and topics that are to be addressed in more in-depth in other
courses and is described in the syllabus in the following way:
This class is designed to provide you with an overview of the field of public administra-
tion. You will explore the historical foundations, the major theoretical, organizational, and
political breakthroughs, and the dynamic tensions inherent to public and nonprofit sector
administration. Special attention will be given to problems arising from political imperatives
generated within a democratic society.
Each week a series of classic and contemporary texts are read and reviewed by the
students. In part, to fill a noticeable void in the literature, the authors co-wrote, along
with Jack Meek, a book on governance networks called: Governance Networks in
Public Administration and Public Policy (Koliba et al. 2010). This book is required
reading. Students are also asked to purchase Shafritz and Hyde’s edited volume,
Classics of Public Administration.
Current events assignments offered through blog posts are undertaken. Weekly
themes include: the science and art of administration; citizens and the administra-
tive state; nonprofit, private, and public sector differences; governance networks;
accountability; and performance management.
During the 2009 reforms of the core curriculum, discrete units on governance
networks and performance management were added to this course. Throughout the
entire course, a complex systems lens is employed to describe and analyze gover-
nance networks and the particular role that performance management systems play
in providing feedback to governance actors. Students are exposed to social network
and system dynamics theory, and asked to apply these lenses to several written cases
taken from the Electronic Hallway. A unit on performance management systems and
their role within fostering organizational learning are provided along with readings
and examples of decision support tools and dashboard platforms currently in use by
government agencies.
Across many units, including units on trends and reforms, ethical and reflective
leadership, citizens and the administrative state, and accountability, the increasing
use of social media and other forms of information technology are discussed. Trends
w.jager@rug.nl

2 Educating Public Managers and Policy Analysts in an Era of Informatics 29
shaping the “e-governance” and “e-government” movements serve as a major focus
on current trends. In addition, students are exposed to current examples of data
visualizations and open data platforms and asked to consider their uses.
2.5 PA 306: Policy Systems
Policy Systems is an entry-level graduate policy course designed to give the MPA
student an overview of the policy process. In 2009, the course was revised to reflect
a more integrated systems focus. The following text provides an overview of the
course:
In particular, the emphasis is placed upon meso-, and macro-scale policy system frame-
works and theories, such as Institutional Analysis and Development Framework, the Multiple
Streams Framework; Social Construction and Policy Design; the Network Approach; Punc-
tuated Equilibrium Theory; the Advocacy Coalition Framework; Innovation and Diffusion
Models and Large-N Comparative Models. Further, students will apply these micro-, meso-
and macro-scale theories to a substantive policy problem that is of interest to a community
partner, which could be a government agency or a non-profit organization. These policy
problems may span, or even cut across, a broad range of policy domains such as (included
but not limited to) economic policy, food policy, environmental policy, defense and foreign
policy, space policy, homeland security, disaster and emergency management, social policy,
transportation policy, land-use policy and health policy.
The core texts for this class are Elinor Ostrom’s, Understanding Institutional Di-
versity, Paul Sabatier’s edited volume, Theories of the Policy Process, and Deborah
Stone’s Policy Paradox: The Art of Political Decision-Making. The course itself is
staged following a micro, to meso, to macro level scale of policy systems framework.
A service-learning element is incorporated. Students are taught to view the policy
process through a systems lens. Zia employs examples of policy systems models us-
ing system dynamics (SD), agent-based modeling (ABM), social network analysis
(SNA), and hybrid approaches throughout the class. By drawing on Ostrom, Sabatier,
and other meso level policy processes as a basis, students are exposed to a number of
“complexity-friendly” theoretical policy frameworks (Koliba and Zia 2013). Appre-
ciating the value of these policy frameworks, students are provided with heuristics
for understanding the flow of information across a system. In addition, students are
shown examples of simulation models of different policy processes, streams, and
systems.
In addition to PA 301 and PA 306, students are also provided an in-depth ex-
ploration of organization theory in PA 302 Organizational Behavior and Change
that is taught through an organizational psychology lens that emphasizes the role of
organizational culture and learning. “Soft systems” approaches are applied. PA 303
Research Methods for Policy Analysis and Program Evaluation exposes students to
a variety of research and program evaluation methodologies with a particular focus
on quantitative analysis techniques. Within PA 305 Public and Nonprofit Budgeting
and Finance, students are taught about evidence-based decision-making and data
management.
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30 C. Koliba and A. Zia
By completing the core curriculum, students are exposed to some of the founda-
tional competencies needed to use and shape policy informatics projects. However,
it is not until students enroll in one of the several electives, that more explicit policy
informatics concepts and applications are taught. Two of these elective courses are
highlighted here. A third, PA 311 Policy Analysis, also exposes students to policy
analyst capacities, but is not highlighted here.
2.6 PA 308: Decision-Making Models
A course designated during the original founding of the University of Vermont
(UVM)-MPA Program, PA 308: Decision-Making Models offers students with a
more advanced look at decision-making theory and modeling. The course is described
by Zia in the following manner:
In this advanced graduate level seminar, we will explore and analyze a wide range of norma-
tive, descriptive and prescriptive decision making models. This course focuses on systems
level thinking to impart problem-solving skills in complex decision-making contexts. Deci-
sion making problems in the real-world public policy, business and management arenas will
be analyzed and modeled with different tools developed in the fields of Decision Analysis,
Behavioral Sciences, Policy Sciences and Complex Systems. The emphasis will be placed
on imparting cutting edge skills to enable students to design and implement multiple criteria
decision analysis models, decision making models under risk and uncertainty and computer
simulation models such as Monte Carlo simulation, system dynamic models, agent based
models, Bayesian decision making models, participatory and deliberative decision making
models, and interactive scenario planning approaches. AnyLogic version 6.6 will be made
available to the students for working with some of these computer simulation models.
2.7 PA 317: Systems Analysis and Strategic Management
Another course designate during the early inception of the program, systems analysis
and strategic management is described by Koliba in the course syllabus as follows:
This course combines systems and network analysis with organizational learning theory and
practices to provide students with a heightened capacity to analyze and effectively operate in
complex organizations and networks. The architecture for the course is grounded in many of
the fundamental conceptual frameworks found in network, systems and complexity analysis,
as well as some of the fundamental frameworks employed within the public administration
and policy studies fields. In this course, strategic management and systems analysis are
linked together through the concept of situational awareness and design principles. Several
units focusing on teaching network analysis tools using UCINet have been incorporated.
One of the key challenges to offering these informatics-oriented electives lies in the
capacities that the traditional MPA students possess to thrive within them. Increas-
ingly, these elective courses are being populated by doctoral and master of science
students looking to apply what they are learning to their dissertations or thesis. Our
MPA program offers a thesis option and we have had some success with these more
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2 Educating Public Managers and Policy Analysts in an Era of Informatics 31
professionally oriented students undertaking high quality informatics focused thesis.
Our experience begs a larger question pertaining to the degree to which the baseline
informatics-savvy public manager capacities lead into more complex policy analysts
competencies associated with the actual design and construction of policy informatics
projects, programs, and platforms.
Table 2.2 provides an overview of where within the curriculum certain policy
informatics capacities are covered. When associated with the class, students are
exposed to the uses of informatics projects, programs, or platforms or provided
opportunities for concrete skill development.
The University of Vermont context is one that can be replicated in other programs.
The capacity of the MPA program to offer these courses hinges on the expertise of
two faculties who teach in the core and these two electives. With additional re-
sources, a more advanced curriculum may be pursued, one that pursues closer ties
with the computer science department (Zia has a secondary appointment) around
curricular alignment. Examples of more advanced curriculum to support the devel-
opment of policy informatics analysts may be found at such institutions as Carnegie
Mellon University, Arizona State University, George Mason University, University
at Albany, Delft University of Technology, Massachusetts Institute of Technology,
among many others. The University of Vermont case suggests, however, that pol-
icy informatics education can be integrated into the main stream with relatively low
resource investments leveraged by strategic relationships with other disciplines and
core faculty with the right skills, training, and vision.
2.8 Conclusion
It is difficult to argue that with the advancement of high speed computing, the dig-
itization of data and the increasing collaboration occurring around the development
of informatics projects, programs, and platforms, that the educational establishment,
particularly at the professional master degree levels, will need to evolve. This chap-
ter lays out a preliminary look at some of the core competencies and capacities that
public managers and policy analysts will need to lead the next generation of policy
informatics integration.
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32 C. Koliba and A. Zia
Table 2.2 Policy informatics capacities covered within the UVM-MPA program curriculum
Course title Policy informatics-savvy public
management capacities covered
Policy informatics analysis
capacities covered
PA 301: Foundations of
public administration
Systems thinking
Policy as process
Performance management
Financial management
Basic communication
Social media/IT/e-governance
Collaborative–cooperative capacity
building
Data visualization and
design
PA 306: Policy systems Systems thinking
Policy as process Basic
communication
Basic modeling skills
PA 302: Organizational
behavior and change
Systems thinking Basic
communication
Collaborative–cooperative capacity
building
PA 303: Research methods
for policy analysis and
program evaluation
Research methods
Basic communication
Data visualization and
design
PA 305: Public and
nonprofit budgeting and
finance
Financial management
Performance management
Basic communication
PA 308: Decision-making
modeling
Systems thinking
Policy as process
Research methods
Performance management
Social media/IT/e-governance
Advanced research methods
Data visualization and
design techniques
Basic modeling skills
PA 311: Policy analysis Systems thinking
Policy as process
Research methods
Performance management
Basic communication
Advanced research methods
Data visualization and
design
Basic modeling skills
PA 317: Systems analysis
and strategic analysis
Systems thinking
Policy as process
Research methods
Performance management
Collaborative–cooperative capacity
building
Basic communication
Social media/IT/e-governance
Data visualization and
design
Basic modeling skills
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2 Educating Public Managers and Policy Analysts in an Era of Informatics 33
2.9 Appendix A: University of Vermont’s MPA Program
Learning Competencies and Capacities
NASPAA core standard UVM-MPA learning capacity
To lead and manage in
public governance
Capacity to understand accountability and democratic theory
Capacity to manage the lines of authority for public, private, and
nonprofit collaboration, and to address sectorial differences to
overcome obstacles
Capacity to apply knowledge of system dynamics and network
structures in PA practice
Capacity to carry out effective policy implementation
To participate in and
contribute to the policy
process
Capacity to apply policy streams, cycles, systems foci upon past,
present, and future policy issues, and to understand how problem
identification impacts public administration
Capacity to conduct policy analysis/evaluation
Capacity to employ quantitative and qualitative research methods for
program evaluation and action research
To analyze, synthesize,
think critically, solve
problems, and make
decisions
Capacity to initiate strategic planning, and apply organizational
learning and development principles
Capacity to apply sound performance measurement and management
practices
Capacity to apply sound financial planning and fiscal responsibility
Capacity to employ quantitative and qualitative research methods for
program evaluation and action research
To articulate and apply a
public service
perspective
Capacity to understand the value of authentic citizen participation in
PA practice
Capacity to understand the value of social and economic equity in
PA practices
Capacity to lead in an ethical and reflective manner
Capacity to achieve cooperation through participatory practices
To communicate and
interact productively
with a diverse and
changing workforce and
citizenry
Capacity to undertake high quality oral, written, and electronically
mediated communication and utilize information systems and media
to advance objectives
Capacity to appreciate the value of pluralism, multiculturalism, and
cultural diversity
Capacity to carry out effective human resource management
Capacity to undertake high quality oral, written, and electronically
mediated communication and utilize information systems and media
to advance objectives
NASPAA Network of Schools of Public Affairs and Administration, UVM University of Vermont,
MPA Master of Public Administration, PA Public administration
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34 C. Koliba and A. Zia
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Argyis C, Schön DA (1996) Organizational learning II: theory, method, and practice. Addison-
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Endsley MR (1995) Toward a theory of situation awareness in dynamic systems. Hum Fact 37(1):32–
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15(4):397–424
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(eds) COMPACT I: public administration in complexity. Emergent, Litchfield Park
Koliba C, Meek J, Zia A (2010) Governance networks in public administration and public policy.
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to manage complex governance networks. Innov J Innov Publ Sect 16(1):1–26 (Article 3).
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bureaucracy, and the study of administration. Westview, Boulder, pp 476–497
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Inf Quart 30(2):123–130
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reform. Georgetown University Press, Washington, DC
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century. Georgetown University Press, Washington, DC
Patton M (2008) Utilization-focused evaluation. Sage, New York
Schick A (1966) The road to PPB: the stages of budget reform. Public Admin Rev 26(4):243–259
Senge PM (1990) The fifth discipline: the art and practice of the learning organization. Doubleday
Currency, New York
Stacey RD (2001) Complex responsive processes in organizations: learning and knowledge creation.
Routledge, London
Willoughby WF (1918) The movement of budgetary reform in the states. D. Appleton, New York
w.jager@rug.nl

Chapter 3
The Quality of Social Simulation: An Example
from Research Policy Modelling
Petra Ahrweiler and Nigel Gilbert
Abstract This chapter deals with the assessment of the quality of a simulation. The
first section points out the problems of the standard view and the constructivist view
in evaluating social simulations. A simulation is good when we get from it what
we originally would have liked to get from the target; in this, the evaluation of
the simulation is guided by the expectations, anticipations, and experience of the
community that uses it. This makes the user community view the most promising
mechanism to assess the quality of a policy-modelling exercise. The second section
looks at a concrete policy-modelling example to test this idea. It shows that the very
first negotiation and discussion with the user community to identify their questions
is highly user-driven, interactive, and iterative. It requires communicative skills,
patience, willingness to compromise on both sides, and motivation to make the
formal world of modellers and the narrative world of practical policy making meet.
Often, the user community is involved in providing data for calibrating the model. It
is not an easy issue to confirm the existence, quality, and availability of data and check
for formats and database requirements. As the quality of the simulation in the eyes of
the user will very much depend on the quality of the informing data and the quality
of the model calibration, much time and effort need to be spent in coordinating this
issue with the user community. Last but not least, the user community has to check
the validity of simulation results and has to believe in their quality. Users have to be
enabled to understand the model, to agree with its processes and ways to produce
results, to judge similarity between empirical and simulated data, etc. Although the
user community view might be the most promising, it is the most work-intensive
mechanism to assess the quality of a simulation. Summarising, to trust the quality
of a simulation means to trust the process that produced its results. This process
includes not only the design and construction of the simulation model itself but also
the whole interaction between stakeholders, study team, model, and findings.
P. Ahrweiler (�)
EA European Academy of Technology and Innovation Assessment GmbH,
Bad Neuenahr-Ahrweiler, Germany
e-mail: Petra.Ahrweiler@ea-aw.de
N. Gilbert
University of Surrey, Guildford, UK
© Springer International Publishing Switzerland 2015 35
M. Janssen et al. (eds.), Policy Practice and Digital Science,
Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_3
w.jager@rug.nl

36 P. Ahrweiler and N. Gilbert
Table 3.1 Comparing simulations
Caffè Nero simulation Science simulation
Target Venetian Café “Real system”
Goal Getting “the feeling” (customers) and
profit (owners) from it
Getting understanding and/or predictions
from it
Model By reducing the many features of a
Venetian Café to a few parameters
By reducing the many features of the
target to a few parameters
Question Is it a good simulation, i.e. do we get
from it what we want?
Is it a good simulation, i.e. do we get
from it what we want?
This chapter deals with the assessment of the quality of a simulation. After dis-
cussing this issue on a general level, we apply and test the assessment mechanisms
using an example from policy modelling.
3.1 Quality in Social Simulation
The construction of a scientific social simulation implies the following process: “We
wish to acquire something from a target entity T. We cannot get what we want from
T directly. So, we proceed indirectly. Instead of T we construct another entity M, the
‘model’, which is sufficiently similar to T that we are confident that M will deliver
(or reveal) the acquired something which we want to get from T. [. . .] At a moment in
time, the model has structure. With the passage of time the structure changes and that
is behaviour. [. . .] Clearly we wish to know the behaviour of the model. How? We
may set the model running (possibly in special sets of circumstances of our choice)
and watch what it does. It is this that we refer to as‘simulation’ of the target” (quoted
with slight modifications from Doran and Gilbert 1994).
We also habitually refer to “simulations” in everyday life, mostly in the sense
that a simulation is “an illusory appearance that manages a reality effect” (cf. Norris
1992), or as Baudrillard put it, “to simulate is to feign to have what one hasn’t”
while “substituting signs for the real” (Baudrillard 1988). In a previous publication
(Ahrweiler and Gilbert 2005), we used the example of the Caffè Nero in Guildford,
50 km southwest of London, as a simulation of a Venetian café—which will serve
as the “real” to illustrate this view. The purpose of the café is to “serve the best
coffee north of Milan”. It tries to give the impression that you are in a real Italian
café—although, most of the time, the weather outside can make the illusion difficult
to maintain.
The construction of everyday simulations like Caffè Nero has some resemblance
to the construction of scientific social simulations (see Table 3.1):
In both cases, we build models from a target by reducing the characteristics of the
latter sufficiently for the purpose at hand; in each case, we want something from the
model we cannot achieve easily from the target. In the case of Caffè Nero, we cannot
simply go to Venice, drink our coffee, be happy, and return. It is too expensive and
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3 The Quality of Social Simulation: An Example from Research Policy Modelling 37
time-consuming. We have to use the simulation. In the case of a science simulation,
we cannot get data from the real system to learn about its behaviour. We have to use
the simulation.
The question, whether one or the other is a good simulation, can therefore be
reformulated as: Do we get from the simulation what we constructed it for?
Heeding these similarities, we shall now try to apply evaluation methods typically
used for everyday simulations to scientific simulation and vice versa. Before doing
so, we shall briefly discuss the “ordinary” method of evaluating simulations called
the “standard view” and its adversary, a constructivist approach asserting, “anything
goes”.
3.1.1 The Standard View
The standard view refers to the well-known questions and methods of verification,
namely whether the code does what it is supposed to do and whether there are
any bugs, and validation, namely whether the outputs (for given inputs/parameters)
resemble observations of the target, although (because the processes being modelled
are stochastic and because of unmeasured factors) identical outputs are not to be
expected, as discussed in detail in Gilbert and Troitzsch (1997). This standard view
relies on a realist perspective because it refers to the observability of reality in order
to compare the “real” with artificial data produced by the simulation.
Applying the standard view to the Caffè Nero example, we can find quantitative
and sometimes qualitative measures for evaluating the simulation. Using quantitative
measures of similarity between it and a “real” Venetian café, we can ask, for example,
• Whether the coffee tastes the same (by measuring, for example, a quality score at
blind tasting),
• Whether the Caffè is a cool place (e.g. measuring the relative temperatures inside
and outside),
• Whether the noise level is the same (using a dB meter for measuring pur-
poses),whether the lighting level is the same (using a light meter), and
• Whether there are the same number of tables and chairs per square metre for the
customers (counting them), and so on.
In applying qualitative measures of similarity, we can again ask:
• Whether the coffee tastes the same (while documenting what comes to mind when
customers drink the coffee),
• Whether the Caffè is a “cool” place (this time meaning whether it is a fashionable
place to hang out),
• Whether it is a vivid, buzzing place, full of life (observing the liveliness of groups
of customers),
• Whether there is the same pattern of social relationships (difficult to opera-
tionalise: perhaps by observing whether the waiters spend their time talking to
the customers or to the other staff), and
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38 P. Ahrweiler and N. Gilbert
• Whether there is a ritual for serving coffee and whether it is felt to be the same as
in a Venetian café.
The assumption lying behind these measures is that there is a “real” café and a
“simulation” café and that in both of these, we can make observations. Similarly,
we generally assume that the theories and models that lie at the base of science
simulations are well grounded and can be validated by observation of empirical
facts. However, the philosophy of science forces us to be more modest.
3.1.1.1 The Problem of Under-determination
Some philosophers of science argue that theories are under-determined by observa-
tional data or experience, that is, the same empirical data may be in accord with
many alternative theories. An adherent of the standard view would respond in that
one important role of simulations (and of any form of model building) is to derive
from theories as many testable implications as possible so that eventually validity
can be assessed in a cumulative process1. Simulation is indeed a powerful tool for
testing theories in that way if we are followers of the standard view.
However, the problem that theories are under-determined by empirical data can-
not be solved by cumulative data gathering: it is more general and therefore more
serious. The under-determination problem is not about a missing quantity of data
but about the relation between data and theory. As Quine (1977) presents it: If it is
possible to construct two or more incompatible theories by relying on the same set
of experimental data, the choice between these theories cannot depend on “empirical
facts”. Quine showed that there is no procedure to establish a relation of uniqueness
between theory and data in a logically exclusive way. This leaves us with an annoying
freedom: “sometimes, the same datum is interpreted by such different assumptions
and theoretical orientations using different terminologies that one wonders whether
the theorists are really thinking of the same datum” (Harbodt 1974, p. 258 f., own
translation).
The proposal mentioned above to solve the under-determination problem by sim-
ulation does not touch the underlying reference problem at all. It just extends the
theory, adding to it its “implications”, hoping them to be more easily testable than
the theory’s core theorems. The general reference between theoretical statement—
be it implication or core theorem—and observed data has not changed by applying
this extension: The point here is that we cannot establish a relation of uniqueness
between the observed data and the theoretical statement. This applies to any segment
of theorising at the centre or at the periphery of the theory on any level—a matter
that cannot be improved by a cumulative strategy.
1 We owe the suggestion that simulation could be a tool to make theories more determined by data
to one of the referees of Ahrweiler and Gilbert (2005).
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3 The Quality of Social Simulation: An Example from Research Policy Modelling 39
3.1.1.2 The Theory-Ladenness of Observations
Observations are supposed to validate theories, but in fact theories guide our ob-
servations, decide on our set of observables, and prepare our interpretation of the
data. Take, for example, the different concepts of two authors concerning Venetian
cafés: For one, a Venetian café is a quiet place to read newspapers and relax with a
good cup of coffee; for the other, a Venetian café is a lively place to meet and talk
to people with a good cup of coffee. The first attribute of these different conceptions
of a Venetian café is supported by one and the same observable, namely the noise
level, although one author expects a low level, the other a high one. The second
attribute is completely different: the first conception is supported by a high number
of newspaper readers, the second by a high number of people talking. Accordingly,
a “good” simulation would mean a different thing for each of the authors. A good
simulation for one would be a poor simulation for the other and vice versa. Here,
you can easily see the influence of theory on the observables. This example could
just lead to an extensive discussion about the “nature” of a Venetian café between
two authors, but the theory-ladenness of observations again leads to more serious
difficulties. Our access to data is compromised by involving theory, with the con-
sequence that observations are not the “bed rock elements” (Balzer et al. 1987) our
theories can safely rely on. At the very base of theory is again theory. The attempt
to validate our theories by “pure” theory-neutral observational concepts is mistaken
from the beginning.
Balzer et al. summarise the long debate about the standard view on this issue as
follows: “First, all criteria of observability proposed up to now are vulnerable to
serious objections. Second, these criteria would not contribute to our task because
in all advanced theories there will be no observational concepts at all—at least if
we take ‘observational’ in the more philosophical sense of not involving any theory.
Third, it can be shown that none of the concepts of an advanced theory can be defined
in terms of observational concepts” (Balzer et al. 1987, p. 48). Not only can you
not verify a theory by empirical observation, but you cannot even be certain about
falsifying a theory. A theory is not validated by “observations” but by other theories
(observational theories). Because of this reference to other theories, in fact a nested
structure, the theory-ladenness of each observation has negative consequences for the
completeness and self-sufficiency of scientific theories (cf. Carrier 1994, pp. 1–19).
These problems apply equally to simulations that are just theories in process.
We can give examples of these difficulties in the area of social simulation. To
compare Axelrod’s The Evolution of Cooperation (Axelrod 1984) and all the subse-
quent work on iterated prisoners’ dilemmas with the “real world”, we would need
to observe “real” IPDs, but this cannot be done in a theory-neutral way. The same
problems arise with the growing body of work on opinion dynamics (e.g. Deffuant
et al. 2000; Ben-Naim et al. 2003; Weisbuch 2004). The latter starts with some sim-
ple assumptions about how agents’ opinions affect the opinions of other agents, and
shows under which circumstances the result is a consensus, polarisation, or fragmen-
tation. However, how could these results be validated against observations without
involving again a considerable amount of theory?
w.jager@rug.nl

40 P. Ahrweiler and N. Gilbert
Important features of the target might not be observable at all. We cannot, for
example, observe learning. We can just use some indicators to measure the conse-
quences of learning and assume that learning has taken place. In science simulations,
the lack of observability of significant features is one of the prime motivations for
carrying out a simulation in the first place.
There are also more technical problems. Validity tests should be “exercised over a
full range of inputs and the outputs are observed for correctness” (Cole 2000, p. 23).
However, the possibility of such testing is rejected: “real life systems have too many
inputs, resulting in a combinatorial explosion of test cases”. Therefore, simulations
have “too many inputs/outputs to be able to test strictly” (Cole 2000, p. 23).
While this point does not refute the standard view in principle but only emphasises
difficulties in execution, the former arguments reveal problems arising from the
logic of validity assessment. We can try to marginalise, neglect, or even deny these
problems, but this will disclose our position as mere “believers” of the standard view.
3.1.2 The Constructivist View
Validating a simulation against empirical data is not about comparing “the real world”
and the simulation output; it is comparing what you observe as the real world with
what you observe as the output. Both are constructions of an observer and his/her
views concerning relevant agents and their attributes. Constructing reality and simu-
lation are just two ways of an observer seeing the world. The issue of object formation
is not normally considered by computer scientists relying on the standard view: data
is “organized by a human programmer who appropriately fits them into the chosen
representational structure. Usually, researchers use their prior knowledge of the na-
ture of the problem to hand-code a representation of the data into a near-optimal
form. Only after all this hand-coding is completed is the representation allowed to
be manipulated by the machine. The problem of representation-formation [. . .] is
ignored” (Chalmers et al. 1995, p. 173).
However, what happens if we question the possibility of validating a simulation
by comparing it with empirical data from the “real world”? We need to refer to the
modellers/observers in order to get at their different constructions. The constructivists
reject the possibility of evaluation because there is no common “reality” we might
refer to. This observer-oriented opponent of the realist view is a nightmare to most
scientists: “Where anything goes, freedom of thought begins. And this freedom of
thought consists of all people blabbering around and everybody is right as long as
he does not refer to truth. Because truth is divisible like the coat of Saint Martin;
everybody gets a piece of it and everybody has a nice feeling” (Droste 1994, p. 50).
Clearly, we can put some central thoughts from this view much more carefully: “In
dealing with experience, in trying to explain and control it, we accept as legitimate
and appropriate to experiment with different conceptual settings, to combine the flow
of experience to different ‘objects”’ (Gellner 1990, p. 75).
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3 The Quality of Social Simulation: An Example from Research Policy Modelling 41
However, this still leads to highly questionable consequences: There seems to
be no way to distinguish between different constructions/simulations in terms of
“truth”, “objectivity”, “validity”, etc. Science is going coffeehouse: Everything is
just construction, rhetoric, and arbitrary talk. Can we so easily dismiss the possibility
of evaluation?
3.1.3 The User Community View
We take refuge at the place we started from: What happens if we go back to the
Venetian café simulation and ask for an evaluation of its performance? It is probably
the case that most customers in the Guildford Caffè Nero have never been to an
Italian café. Nevertheless, they manage to “evaluate” its performance—against their
concept of an Italian café that is not inspired by any “real” data. However, there is
something “real” in this evaluation, namely the customers, their constructions, and
a “something” out there, which everybody refers to, relying on some sort of shared
meaning and having a “real” discussion about it. The philosopher Searle shows in
his work on the Construction of Social Reality (Searle 1997) how conventions are
“real”: They are not deficient for the support of a relativistic approach because they
are constructed.
Consensus about the “reality observed by us” is generated by an interaction pro-
cess that must itself be considered real. At the base of the constructivist view is a
strong reference to reality, that is, conventions and expectations that are socially cre-
ated and enforced. When evaluating the Caffè Nero simulation, we can refer to the
expert community (customers, owners) who use the simulation to get from it what
they would expect to get from the target. A good simulation for them would satisfy
the customers who want to have the “Venetian feeling” and would satisfy the owners
who want to get the “Venetian profit”.
For science, equally, the foundation of every validity discussion is the ordinary
everyday interaction that creates an area of shared meanings and expectations. This
area takes the place left open by the under-determination of theories and the theo-
reticity problem of the standard view.2 Our view comes close to that of empirical
epistemology, which points out that the criteria for quality assessment “do not come
from some a priori standard but rest on the description of the way research is actually
conducted” (Kértesz 1993, p. 32).
2 Thomas Nickles claims new work opportunities for sociology at this point: “the job of phi-
losophy is simply to lay out the necessary logico-methodological connections against which the
under-determination of scientific claims may be seen; in other words, to reveal the necessity of so-
ciological analysis. Philosophy reveals the depths of the under-determination problem, which has
always been the central problem of methodology, but is powerless to do anything about it. Under-
determination now becomes the province of sociologists, who see the limits of under-determination
as the bounds of sociology. Sociology will furnish the contingent connections, the relations, which
a priori philosophy cannot” (Nickles 1989, p. 234 f.).
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42 P. Ahrweiler and N. Gilbert
If the target for a social science simulation is itself a construction, then the simu-
lation is a second-order construction. In order to evaluate the simulation, we can rely
on the ordinary (but sophisticated) institutions of (social) science and their practice.
The actual evaluation of science comes from answers to questions such as: Do others
accept the results as being coherent with existing knowledge? Do other scientists use
it to support their work? Do other scientists use it to inspire their own investigations?
An example of such validity discourse in the area of social simulation is the
history of the tipping model first proposed by Schelling, and now rather well known
in the social simulation community. The Schelling model purports to demonstrate the
reasons for the persistence of urban residential segregation in the USA and elsewhere.
It consists of a grid of square cells, on which are placed agents, each either black or
white. The agents have a “tolerance” for the number of agents of the other colour
in the surrounding eight cells that they are content to have around them. If there are
“too many” agents of the other colour, the unhappy agents move to other cells until
they find a context in which there are a tolerable number of other-coloured agents.
Starting with a random distribution, even with high levels of tolerance, the agents
will still congregate into clusters of agents of the same colour. The point Schelling
and others have taken from this model is that residential segregation will form and
persist even when agents are rather tolerant.
The obvious place to undertake a realist validation of this model is a US city. One
could collect data about residential mobility and, perhaps, on “tolerance”. However,
the exercise is harder than it looks. Even US city blocks are not all regular and
square, so the real city does not look anything like the usual model grid. Residents
move into the city from outside, migrate to other cities, are born and die, so the tidy
picture of mobility in the model is far from the messy reality. Asking residents how
many people of the other colour they would be tolerant of is also an exercise fraught
with difficulty: the question is hypothetical and abstract, and answers are likely
to be biased by social desirability considerations. Notwithstanding these practical
methodological difficulties, some attempts have been made to verify the model. The
results have not provided much support. For instance, Benenson (2005) analysed
residential distribution for nine Israeli cities using census data and demonstrated that
whatever the variable tested—family income, number of children, education level—
there was a great deal of ethnic and economic heterogeneity within neighbourhoods,
contrary to the model’s predictions.
This apparent lack of empirical support has not, however, dimmed the fame of the
model. The difficulty of obtaining reliable data provides a ready answer to doubts
about whether the model is “really” a good representation of urban segregation dy-
namics. Another response has been to elaborate the model at the theoretical level.
For instance, Bruch (2005) demonstrates that clustering only emerges in Schelling’s
model for discontinuous functional forms for residents’ opinions, while data from
surveys suggest that people’s actual decision functions for race are continuous. She
shows that using income instead of race as the sorting factor also does not lead to
clustering, but if it is assumed that both race and income are significant, segregation
appears. Thus, the model continues to be influential, although it has little or no em-
pirical support, because it remains a fruitful source for theorising and for developing
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3 The Quality of Social Simulation: An Example from Research Policy Modelling 43
new models. In short, it satisfies the criterion that it is “valid” because it generates
further scientific work.
Summarising the first part of this chapter, we have argued that a simulation is good
when we get from it what we originally would have liked to get from the target. It is
good if it works. As Glasersfeld (1987, p. 429) puts it: “Anything goes if it works”.
The evaluation of the simulation is guided by the expectations, anticipations and
experience of the community that uses it—for practical purposes (Caffè Nero), or
for intellectual understanding and for building new knowledge (science simulation).
3.2 An Example of Assessing Quality
In this part, we will apply and test the assessment mechanisms outlined using as an
example our work with the simulating knowledge dynamics in innovation networks
(SKIN) model in its application to research policy modelling.
There are now a number of policy-modelling studies using SKIN (Gilbert
et al. 2014). We will here refer to just one recent example, on the impact, assess-
ment and ex-ante evaluation of European funding policies in the Information and
Communication Technologies (ICT) research domain (Ahrweiler et al. 2014b).
3.2.1 A Policy-Modelling Application of SKIN
The basic SKIN model has been described and discussed in detail elsewhere
(e.g. Pyka et al. 2007; Gilbert et al. 2007; Ahrweiler et al. 2011). On its most general
level, SKIN is an agent-based model where agents are knowledge-intensive organi-
sations, which try to generate new knowledge by research, be it basic or applied, or
creating new products and processes by innovation processes. Agents are located in
a changing and complex social environment, which evaluates their performance; e.g.
the market if the agents target innovation or the scientific community if the agents
target publications through their research activities. Agents have various options to
act: each agent has an individual knowledge base called its “kene” (cf. Gilbert 1997),
which it takes as the source and basis for its research and innovation activities. The
agent kene is not static: the agent can learn, either alone by doing incremental or
radical research, or from others, by exchanging and improving knowledge in partner-
ships and networks. The latter feature is important, because research and innovation
happens in networks, both in science and in knowledge-intensive industries. This
is why SKIN agents have a variety of strategies and mechanisms for collaborative
arrangements, i.e. for choosing partners, forming partnerships, starting knowledge
collaborations, creating collaborative outputs, and distributing rewards. Summaris-
ing, usually a SKIN application has agents interacting on the knowledge level and
on the social level while both levels are interconnected. It is all about knowledge and
networks.
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44 P. Ahrweiler and N. Gilbert
This general architecture is quite flexible, which is why the SKIN model has been
called a “platform” (cf. Ahrweiler et al. 2014a), and has been used for a variety of
applications ranging from the small such as simulating the Vienna biotech cluster
(Korber and Paier 2014) to intermediate such as simulating the Norwegian defence
industry (Castelacci et al. 2014), to large-scale applications such as the EU-funded
ICT research landscape in Europe (Ahrweiler et al. 2014b). We will use the latter
study as an example after explaining why the SKIN model is appropriate for realistic
policy modelling in particular.
The birth of the SKIN model was inspired by the idea of bringing a theory on
innovation networks, stemming mainly from innovation economics and economic so-
ciology, onto the computer—a computer theory, which can be instantiated, calibrated,
tested, and validated by empirical data. In 1998, the first EU project developing the
model “Simulating Self-Organizing Innovation Networks” (SEIN) consisted of a
three-step procedure: theory formation, empirical research collecting data both on
the quantitative and on the case study level, and agent-based modelling implementing
the theory and using the data to inform the model (Pyka et al. 2003).
This is why the SKIN model applications use empirical data and claim to be
“realistic simulations” insofar as the aim is to derive conclusions by “inductive the-
orising”. The quality of the SKIN simulation derives from an interaction between
the theory underlying the simulation and the empirical data used for calibration and
validation.
In what way does the SKIN model handle empirical data? We will now turn
to our policy-modelling example to explain the data-to-model workflow, which is
introduced in greater detail in Schilperoord and Ahrweiler (2014).
3.2.1.1 Policy Modelling for Ex-ante Evaluation of EU Funding Programmes
The INFSO-SKIN application, developed for the Directorate General Information
Society and Media of the European Commission (DG INFSO), was intended to help
to understand and manage the relationship between research funding and the goals
of EU policy. The agents of the INFSO-SKIN application are research institutions
such as universities, large diversified firms or small and medium-sized enterprises
(SMEs). The model (see Fig. 3.1) simulated real-world activity in which the calls
of the commission specify the composition of consortia, the minimum number of
partners, and the length of the project; the deadline for submission; a range of
capabilities, a sufficient number of which must appear in an eligible proposal; and
the number of projects that will be funded. The rules of interaction and decision
implemented in the model corresponded to Framework Programme (FP) rules; to
increase the usefulness for policy designers, the names of the rules corresponded
closely to FP terminology. For the Calls 1–6 that had occurred in FP7, the model
used empirical information on the number of participants and the number of funded
projects, together with data on project size (as measured by participant numbers),
duration and average funding. Analysis of this information produced data on the
functioning of, and relationships within, actual collaborative networks within the
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3 The Quality of Social Simulation: An Example from Research Policy Modelling 45
Fig. 3.1 Flowchart of INFSO-SKIN
context of the FP. Using this data in the model provided a good match with the
empirical data from EU-funded ICT networks in FP7: the model accurately reflected
what actually happened and could be used as a test bed for potential policy choices
(cf. Ahrweiler et al. 2014b).
Altering elements of the model that equate to policy interventions, such as the
amount of funding, the size of consortia, or encouraging specific sections of the
research community, enabled the use of INFSO-SKIN as a tool for modelling and
evaluating the results of specific interactions between policies, funding strategies
and agents. Because changing parameters within the model is analogous to applying
different policy options in the real world, the model could be used to examine the
likely real-world effects of different policy options before they were implemented.
3.2.1.2 The Data-to-Model Workflow
The first contact with “the real world” occurred in the definition phase of the project.
What do the stakeholders want to know in terms of policies for a certain research or
innovation network? Identifying relevant issues, discussing interesting aspects about
them, forming questions and suggesting hypotheses for potential answers formed a
first important step. This step was intended to conclude with a set of questions and a
corresponding set of designs for experiments using the model that could answer those
questions. This was an interactive and participative process between the study team,
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46 P. Ahrweiler and N. Gilbert
which knew about the possibilities and limitations of the model, and the stakeholders,
who could be assumed to know what are the relevant issues in their day-to-day
practice of policy making.
After discussing the evaluative questions for the ex-ante evaluation part of this
study with the stakeholders from DG INFSO, the following questions were singled
out for experiments:
1. What if there are no changes, and funding policies of DG INFSO continued in
Horizon 2020 as they were in FP7?
2. What if there are changes to the currently eight thematic areas funded in the ICT
domain prioritising certain areas in Horizon 2020?
3. What if there are changes to the instruments of funding and fund larger/smaller
consortia in Horizon 2020 than in FP7?
4. What if there are interventions concerning the scope or outreach of funding
providing much more/much less resource to more/fewer actors?
5. What if there are interventions concerning the participation of certain actors in
the network (e.g. SMEs)?
The next step (see Fig. 3.2) was to collect relevant data to address these questions and
hypotheses. The issues were not different from the ones every empirical researcher
is confronted with. To identify relevant variables for operationalising hypotheses, to
be as simple as possible but as detailed as necessary for description and explana-
tion, is in line with the requirements of all empirical social research. For SKIN, the
most important data are about knowledge dynamics (e.g. knowledge flows, amount
of knowledge, and diversity of knowledge) and their indicators (e.g. publications,
patents, and innovative ideas), and about dynamics concerning actors, networks, their
measures, and their performance (e.g. descriptive statistics about actors, network
analysis measures, and aggregate performance data).
These data were used to calibrate the initial knowledge bases of the agents, the
social configurations of agents (“starting networks”), and the configuration of an
environment at a given point in time. DG INFSO provided the data needed to calibrate
the knowledge bases of the agents (in this case the research organisations in the
European research area), the descriptive statistics on agents and networks and their
interactions (in this case data on funded organisations and projects in ICT under
FP7).
The time series data were used to validate the simulations by comparing the
empirical data with the simulation outputs. Once we were satisfied with the model
performance in that respect, experiments were conducted and the artificially produced
data analysed and interpreted. The stakeholders were again invited to provide their
feedback and suggestions about how to finetune and adapt the study to their changing
user requirements as the study proceeded.
The last step was again stakeholder-centred as it involved visualisation and com-
munication of data and results. We had to prove the credibility of the work and the
commitment of the stakeholders to the policy-modelling activity.
We worked from an already existing application of the SKIN model adapted to the
European research area (Scholz et al. 2010), implemented the scenarios according
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3 The Quality of Social Simulation: An Example from Research Policy Modelling 47
Baseline
Thematic
change
Instruments
change
Funding level
change
Participants
chenge
Evaluative questions
Horizon 2020
INFSO FP7
Database
Calls, Themes,
Participants,
Projects
21
Network Vis.
& Statistics
Gephi
Scenario
Development Tool
K”5″(Java
3 4
Participants
impacts
Proposals
impacts
Projects
impacts
Knowlwdge
impacts
Network
impacts
SKIN
model
Netlogo & lava
Simulation
Database
CSV
Computational
Policy Lab
MySQL
Calls, Themes,
Participants,
Proposals, Projects,
Knowlwdge flows
Fig. 3.2 Horizon 2020 study workflow (Schilperoord and Ahrweiler 2014). First (on the left), a set
of issues was isolated, in discussion with stakeholders. Data describing the network of FP7 projects
and participants, by theme and Call, obtained from DG INFSO were entered into a database.
These data were used to calibrate the INFSO-SKIN model. This model was then used to generate
simulated data under various policy options. The simulated data were fed into a second database
and visualised using additional network visualisation and statistical software in order to assess the
expected impacts of those policy options
to the evaluative questions, and produced artificial data as output of the simulations.
The results are reported in the final report presented to the European Cabinet, and
were communicated to the stakeholders at DG INFSO.
3.2.2 The INFSO-SKIN Example as Seen by the Standard View
The standard view refers to verification, namely whether the code does what it is sup-
posed to do, and validation, namely whether the outputs (for given inputs/parameters)
sufficiently resemble observations of the target. To aid in verifying the model, it was
completely recoded in another programming language and the two implementations
cross-checked to ensure that they generated the same outputs given the same inputs.
To enable validation of the model, we needed to create a simulation resembling
the stakeholders’ own world as they perceived it. The simulation needed to create the
effect of similar complexity, similar structures and processes, and similar objects and
options for interventions. To be under this similarity threshold would have led to the
rejection of the model as a “toy model” that is not realistic and is under-determined
by empirical data.
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48 P. Ahrweiler and N. Gilbert
In the eyes of these stakeholders, the more features of the model that can be
validated against empirical data points, the better. Of course, there will always be
an empirical “under-determination” of the model due to the necessary selection and
abstraction process of model construction, empirical unobservables, missing data for
observables, random features of the model, and so on. However, to find the “right”
trade-off between empirical under-determination and model credibility was a crucial
issue in the discussions between the study team and the stakeholders.
3.2.3 The INFSO-SKIN Example as Seen by the Constructivist
View
The strength of a modelling methodology lies in the opportunity to ask what-if
questions (ex-ante evaluation), an option that is normally not easily available in
the policy-making world. INFSO-SKIN uses scenario modelling as a worksite for
“reality constructions”, in line with Gellner’s statement quoted above about the
constructivist approach: “In dealing with experience, in trying to explain and control
it, we accept as legitimate and appropriate to experiment with different conceptual
settings, to combine the flow of experience to different ‘objects”’ (Gellner 1990,
p. 75). Scenario modelling was employed in the study both for the impact assessment
of existing funding policies, where we measured the impact of policy measures by
experimenting with different scenarios where these policies are absent, changed or
meet different conditions, and for ex-ante evaluation, where we developed a range of
potential futures for the European Research Area in ICT by asking what-if questions.
These are in-silico experiments that construct potential futures. Is this then a
relativist approach where “anything goes”, because everything is just a construction?
For the general aspects of this question, we refer to Part I of this article. There we talk
about the “reality requirements” of the constructivist approach, which mediates its
claims. For the limits of constructivist ideas applied to SKIN, we refer to Sect. 2.1.
3.2.4 The INFSO-SKIN Example as Seen by the User Community
View
The user community view is the most promising, although the most work-intensive
mechanism to assess the quality of this policy-modelling exercise.
3.2.4.1 Identifying User Questions
In our example, SKIN was applied to a tender study with a clear client demand behind
it, where the questions the simulation needs to answer were more or less predefined
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3 The Quality of Social Simulation: An Example from Research Policy Modelling 49
from the onset of the project. Enough time should, however, be dedicated to identi-
fying and discussing the exact set of questions the stakeholders of the work want to
see addressed. We found that the best way to do this is applying an iterative process
of communication between study team and clients, where stakeholders learn about
the scope and applicability of the methods, and where researchers get acquainted
with the problems policy makers have to solve and with the kind of decisions for
which sound background information is needed. This iterative process should result
in an agreed set of questions for the simulation, which will very often decisively
differ from the set proposed at the start of the study. In our example, the so-called
“steering committee” was assigned to us by the European Commission consisting of
policy makers and evaluation experts of DG INFSO.
There are various difficulties and limitations to overcome in identifying user ques-
tions. In the case of the DG INFSO study, although the questions under study
were outlined in the Tender Specifications in great detail, this was a complicated
negotiation process where the stakeholder group:
• Had to find out about the exact nature and direction of their questions while they
talked to the study team;
• Had questioned the original set of the Tender Specifications in the meantime and
negotiated among each other for an alternative set;
• Did not share the same opinion about what questions should be in the final sample,
and how potential questions should be ranked in importance;
• Did not share the same hypotheses about questions in the final sample.
The specification of evaluative questions might be the first time stakeholders talk to
each other and discuss their viewpoints.
What is the process for identifying user questions for policy modelling? In the
INFSO-SKIN application, the following mechanism was used by the study team and
proved to be valuable:
• Scan written project specification by client (in this case the Tender Specifications
of DG INFSO) and identify the original set of questions;
• Do a literature review and context analysis for each question (policy background,
scope, meaning, etc.) to inform the study team;
• Meet stakeholders to get their views on written project specifications and their
view on the context of questions; inform the stakeholders about what the model is
about, what it can and cannot do; discuss until stakeholder group and study team
is “on the same page”;
• Evaluate the meeting and revise original set of questions if necessary (probably
an iterative process between study team and different stakeholders individually
where study team acts as coordinator and mediator of the process);
• Meet stakeholders to discuss the final set of questions, get their written consent
on this, and get their hypotheses concerning potential answers and potential ways
to address the questions;
• Evaluate the meeting and develop experiments that are able to operationalise the
hypotheses and address the questions;
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50 P. Ahrweiler and N. Gilbert
• Meet stakeholders and get their feedback and consent that the experiments meet
questions/hypotheses;
• Evaluate the meeting and refine the experimental setup concerning the final set
of questions.
This negotiation and discussion process is highly user-driven, interactive, and itera-
tive. It requires communicative skills, patience, willingness to compromise on both
sides, and motivation to make both ends meet—the formal world of modellers and the
narrative world of policy making in practice. The process is highly time-consuming.
In our example, we needed about 6 months of a 12-month-contract research study to
get to satisfactory results on this first step.
3.2.4.2 Getting Their Best: Users Need to Provide Data
The study team will know best what types of empirical data are needed to inform
the policy modelling. In SKIN, data availability is an important issue, because the
findings need to be evidence-based and realistic. This is in the best interest of the
stakeholders, who need to trust the findings. This will be the more likely to the extent
that the simulated data resembles the empirical data known to the user (see Sect. 2.1).
However, the study team might discover that the desired data is not available, either
because it does not exist or because it is not willingly released by the stakeholders
or whoever holds it.
In our example, the stakeholders were data collectors on a big scale themselves.
The evaluation unit of DG INFSO employs a data collection group, which provides
information about funded projects and organisations at a detailed level. Furthermore,
the DG is used to provide data to the study teams of the projects they contract for
their evaluation projects. Consequently we benefitted from having a large and clean
database concerning all issues the study team was interested in. However, it was still
an issue to confirm the existence, quality and availability of the data and check for
formats and database requirements. Even if the data is there in principal, enough
time should be reserved for data management issues. The quality of the simulation
in the eyes of the user will very much depend on the quality of the informing data
and the quality of the model calibration.
What would have been the more common process if the study team had not struck
lucky as in our example? In other SKIN applications, the following mechanism was
used by the study team and proved to be valuable (the ones with asterisks apply to
our INFSO-SKIN example as well):
• Identify the rough type of data required for the study from the project specifications
• Estimate financial resources for data access in the proposal of project to
stakeholders (this can sometimes happen in interaction with the funding body);
• After the second meeting with stakeholders (see Sect. 2.3.1), identify relevant
data concerning variables to answer study questions and address/test hypotheses
of Sect. 2.3.1*;
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3 The Quality of Social Simulation: An Example from Research Policy Modelling 51
• Communicate exact data requirements to those stakeholders who are experts on
their own empirical data environment*;
• Review existing data bases including the ones stakeholders might hold or can get
access to*;
• Meet stakeholders to discuss data issues; help them understand and agree on the
scope and limitations of data access*;
• If needed and required by stakeholders, collect data;
• Meet stakeholders to discuss the final database;
• Evaluate the meeting and develop data-to-model procedures*.
3.2.4.3 Interacting with Users to Check the Validity of Simulation Results
The stakeholders put heavy demands on the study team concerning understanding
and trusting the simulation findings. The first and most important is that the clients
want to understand the model. To trust results means to trust the process that produced
them. Here, the advantage of the adapted SKIN model is that it relies on a narrative
that tells the story of the users’ every-day world of decision-making (see Sect. 2.1.1).
In the SKIN model, a good example for “reality” requirements is the necessity to
model the knowledge and behaviour of agents. Blackboxing knowledge of agents
or creating merely reactive simple agents would not have been an option, because
stakeholders do not think the world works that way.
The SKIN model is based on empirical quantitative and qualitative research in
innovation economics, sociology, science and technology studies, and business stud-
ies. Agents and behaviours are informed by what we know about them; the model
is calibrated by data from this research. We found that there is a big advantage in
having a model where stakeholders can recognise the relevant features they see at
work in their social contexts. In setting up and adapting the model to study needs,
stakeholders can actively intervene and ask for additional agent characteristics or
behavioural rules; they can refine the model and inform blackbox areas where they
have information on the underlying processes.
However, here again, we encountered the diversity of stakeholder preferences.
Different members of the DG INFSO Steering Committee opted for different changes
and modifications of the model. Some were manageable with given time constraints
and financial resources; some would have outlived the duration of the project if
realised. The final course of action for adapting the model to study needs was the
result of discussions between stakeholders about model credibility and increasing
complexity and of discussions between stakeholders and the study team concerning
feasibility and reducing complexity.
Once the stakeholders were familiar with the features of the model and had con-
tributed to its adaptation to study requirements, there was an initial willingness to
trust model findings. This was strengthened by letting the model reproduce FP7 data
as the baseline scenario that all policy experiments would be benchmarked against.
If the networks created by real life and those created by the agent-based model cor-
respond closely, the simulation experiments can be characterized as history-friendly
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52 P. Ahrweiler and N. Gilbert
experiments, which reproduce the empirical data and cover the decisive mechanisms
and resulting dynamics of the real networks (see standard view).
In presenting the results of the INFSO-SKIN study, however, it became clear
that there were, again, certain caveats coming from the user community. The policy
analysts did not want to look at a multitude of tables and scan through endless
numbers of simulation results for interesting parameters; nor did they expect to
watch the running model producing its results, because a typical run lasted 48 hours.
Presenting results in an appealing and convincing way required visualisations and
interactive methods where users could intuitively understand what they see, had
access to more detailed information if wanted, e.g. in a hyperlink structure, and
could decide themselves in which format, in which order and in which detail they
want to go through findings. This part of the process still needs further work: new
visualisation and interactive technologies can help to make simulation results more
accessible to stakeholders.
This leads to the last issue to be discussed in this section. What happens after
the credibility of simulation results is established? In the INFSO-SKIN study, the
objective was policy advice for Horizon 2020. The stakeholders wanted the study
team to communicate the results as “recommendations” rather than as “findings”.
They required a so-called “utility summary” that included statements about what
they should do in their policy domain justified according to the results of the study.
Here the study team proved to be hesitant—not due to a lack of confidence in their
model, but due to the recognition of its predictive limitations and a reluctance to
formulate normative statements, which were seen as a matter of political opinion
and not a responsibility of a scientific advisor. The negotiation of the wording in the
Utility Summary was another instance of an intense dialogue between stakeholders
and study team. Nevertheless, the extent to which the results influenced or were
somehow useful in the actual political process of finalising Horizon 2020 policies
was not part of the stakeholder feedback after the study ended and is still not known
to us. The feedback consisted merely of a formal approval that we had fulfilled the
project contract.
3.3 Conclusions
To trust the quality of a simulation means to trust the process that produced its results.
This process is not only the one incorporated in the simulation model itself. It is the
whole interaction between stakeholders, study team, model, and findings.
The first section of this contribution pointed out the problems of the Standard
View and the constructivist view in evaluating social simulations. We argued that
a simulation is good when we get from it what we originally would have liked to
get from the target; in this, the evaluation of the simulation would be guided by the
expectations, anticipations, and experience of the community that uses it. This makes
the user community view the most promising mechanism to assess the quality of a
policy-modelling exercise.
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3 The Quality of Social Simulation: An Example from Research Policy Modelling 53
The second section looked at a concrete policy-modelling example to test this
idea. It showed that the very first negotiation and discussion with the user commu-
nity to identify their questions were highly user-driven, interactive, and iterative. It
required communicative skills, patience, willingness to compromise on both sides,
and motivation to link the formal world of modellers and the narrative world of policy
making in practice.
Often, the user community is involved in providing data for calibrating the model.
It is not an easy issue to confirm the existence, quality, and availability of the data and
check for formats and database requirements. Because the quality of the simulation
in the eyes of the user will depend on the quality of the informing data and the quality
of the model calibration, much time and effort need to be spent in coordinating this
issue with the user community.
Last but not least, the user community has to check the validity of simulation
results and has to believe in their quality. Users have to be helped to understand the
model, to agree with its processes and ways to produce results, to judge similarity
between empirical and simulated data, etc.
The standard view is epistemologically questionable due to the two problems
of under-determination of theory and of theory-ladenness of observations; the con-
structivist view is difficult due to its inherent relativism, which annihilates its own
validity claims. The user community view relies on social model building and model
assessment practices and, in a way, bridges the two other views, because it rests
on the realism of these practices. This is why we advocate its quality assessment
mechanisms.
Summarising, in our eyes, the user community view might be the most promis-
ing, but is definitely the most work-intensive mechanism to assess the quality of a
simulation. It all depends on who the user community is and whom it consists of: if
there is more than one member, the user community will never be homogenous. It is
difficult to refer to a “community”, if people have radically different opinions.
Furthermore, there are all sorts of practical contingencies to deal with. People
might not be interested, or they might not be willing or able to dedicate as much of
their time and attention to the study as needed. There is also the time dimension: the
users at the end of a simulation project might not be the same as those who initiated it,
as a result of job changes, resignations, promotions, and organisational restructuring.
Moreover, the user community and the simulation modellers may affect each other,
with the modellers helping in some ways to construct a user community in order to
solve the practical contingencies that get in the way of assessing the quality of the
simulation, while the user community may in turn have an effect on the modellers
(not least in terms of influencing the financial and recognition rewards the modellers
receive).
If trusting the quality of a simulation indeed means trusting the process that pro-
duced its results, then we need to address the entire interaction process between user
community, researchers, data, model, and findings as the relevant assessment mech-
anism. Researchers have to be aware that they are codesigners of the mechanisms
they need to participate in with the user community for assessing the quality of a
social simulation.
w.jager@rug.nl

54 P. Ahrweiler and N. Gilbert
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w.jager@rug.nl

Chapter 4
Policy Making and Modelling in a Complex
World
Wander Jager and Bruce Edmonds
Abstract In this chapter, we discuss the consequences of complexity in the real world
together with some meaningful ways of understanding and managing such situations.
The implications of such complexity are that many social systems are unpredictable
by nature, especially when in the presence of structural change (transitions). We
shortly discuss the problems arising from a too-narrow focus on quantification in
managing complex systems. We criticise some of the approaches that ignore these
difficulties and pretend to predict using simplistic models. However, lack of pre-
dictability does not automatically imply a lack of managerial possibilities. We will
discuss how some insights and tools from “complexity science” can help with such
management. Managing a complex system requires a good understanding of the
dynamics of the system in question—to know, before they occur, some of the real
possibilities that might occur and be ready so they can be reacted to as responsively
as possible. Agent-based simulation will be discussed as a tool that is suitable for
this task, and its particular strengths and weaknesses for this are discussed.
4.1 Introduction
Some time ago, one of us (WJ) attended a meeting of specialists in the energy sector.
A former minister was talking about the energy transition, advocating for directing
this transition; I sighed, because I realized that the energy transition, involving a
multitude of interdependent actors and many unforeseen developments, would make
a planned direction of such a process a fundamental impossibility. Yet I decided not to
interfere, since my comment would have required a mini lecture on the management
of complex systems, and in the setting of this meeting this would have required too
much time. So the speaker went on, and one of the listeners stood up and asked, “But
W. Jager (�)
Groningen Center of Social Complexity Studies, University Groningen, Groningen,
The Netherlands
e-mail: w.jager@rug.nl
B. Edmonds
Manchester Metropolitan University, Manchester, UK
© Springer International Publishing Switzerland 2015 57
M. Janssen et al. (eds.), Policy Practice and Digital Science,
Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_4
w.jager@rug.nl

58 W. Jager and B. Edmonds
Fig. 4.1 Double pendulum.
(Source: Wikipedia)
sir, what if the storage capacity of batteries will drastically improve?” The speakers
answered, “this is an uncertainty we cannot include in our models, so in our transition
scenarios we don’t include such events”. This remark made clear that, in many cases,
policymakers are not aware of the complexities in the systems they operate in, and are
not prepared to deal with surprises in systems. Because the transitional idea is being
used very frequently to explain wide-ranging changes related to the transformation
of our energy system, and the change towards a sustainable society, it seems relevant
to address the issue of complexity in this chapter, and discuss the implications for
policy making in complex behaving system. After explaining what complexity is,
we will discuss the common mistakes being made in managing complex systems.
Following that, we will discuss the use of models in policy making, specifically
addressing agent-based models because of their capacity to model social complex
systems that are often being addressed by policy.
4.2 What is Complexity?
The word “complexity” can be used to indicate a variety of kinds of difficulties.
However, the kind of complexity we are specifically dealing with in this chapter
is where a system is composed of multiple interacting elements whose possible
behavioural states can combine in ways that are hard to predict or characterise. One
of the simplest examples is that of a double pendulum (Fig 4.1).
w.jager@rug.nl

4 Policy Making and Modelling in a Complex World 59
Although only consisting of a few parts connected by joints, it has complex and un-
predictable behaviour when set swinging under gravity. If this pendulum is released,
it will move chaotically due to the interactions between the upper (θ1) and lower (θ2)
joint. Whereas it is possible to formally represent this simple system in detail, e.g.
including aspects such as air pressure, friction in the hinge, the exact behaviour of
the double pendulum is unpredictable.1 This is due to the fundamental uncertainty
of the precise position of its parts2and the unsolvability of the three-body problem as
proven by Bruns and Poincaré in 1887. Just after release, its motion is predictable to
a considerable degree of accuracy, but then starts to deviate from any prediction until
it is moving in a different manner. Whereas the precise motion at these stages is not
predictable, we know that after a while, the swinging motion will become less erratic,
and ultimately it will hang still (due to friction). This demonstrates that even in very
simple physical systems, interactions may give rise to complex behaviour, expressed
in different types of behaviour, ranging from very stable to chaotic. Obviously, many
physical systems are much more complicated, such as our atmospheric system. As
can be expected, biological or social systems also display complex behaviour be-
cause they are composed of large numbers of interacting agents. Also, when such
systems are described by a simple set of equations, complex behaviour may arise.
This is nicely illustrated by the “logistic equation”, which was originally introduced
as a simple model of biological populations in a situation of limited resources (May
1976). Here the population, x, in the next year (expressed as a proportion of its max-
imum possible) is determined based on the corresponding value in the last year as
rx(1-x), where r is a parameter (the rate of unrestrained population increase). Again,
this apparently simple model leads to some complex behaviour. Figure 4.2 shows
the possible long-term values of x for different values of r, showing that increasing
r creates more possible long-term states for x. Where on the left hand side (r< 3.0) the state of x is fixed, at higher levels the number of possible states increases with the number of states increasing rapidly until, for levels of r above 3.6, almost any state can occur, indicating a chaotic situation. In this case, although the system may be predictable under some circumstances (low r), in others it will not be (higher r). What is remarkable is that, despite the inherent unpredictability of their environ- ment, organisms have survived and developed intricate webs of interdependence in terms of their ecologies. This is due to the adaptive capacity of organisms, allowing them to self-organise. It is exactly this capacity of organisms to adapt to changing cir- cumstances (learning) that differentiates ‘regular’ complex systems from ‘complex adaptive systems’ (CAS). Hence complex adaptive systems have a strong capacity to self-organise, which can be seen in, i.e. plant growth, the structure of ant nests and the organisation of human society. Yet these very systems have been observed to exist in both stable and unstable stages, with notable transitions between these 1 Obviously predictions can always be made, but it has been proved analytically that the predictive value of models is zero in these cases. 2 Even if one could measure them with extreme accuracy, there would never be complete accuracy due to the uncertainty theorem of Heisenberg (1927). w.jager@rug.nl 60 W. Jager and B. Edmonds Fig. 4.2 Bifurcation diagram. (Source: Wikipedia) stages. Ecological science has observed that major transitions in ecological systems towards a different regime (transition) are often preceded by increased variances, slower recovery from small perturbations (critical slowing down) and increased re- turn times (Boettiger and Hastings 2012; Dai and Vorselen et al. 2012; Dakos and Carpenter et al. 2012). A classic example here is that of the transition from a clear lake to a turbid state due to eutrophication. Here an increase in mineral and organic nutrients in the water gives rise to the growth of plants, in particular algae. In the stage preceding to a transition, short periods of increased algal blooms may occur, decreasing visibility and oxygen levels, causing the population of top predating fish hunting on eyesight to decrease, causing a growth in populations of other species, etc. The increased variance (e.g. in population levels of different species in the lake) indicates that a regime shift is near, and that the lake may radically shift from a clear state to a turbid state with a complete different ecosystem, with an attendant loss of local species. The hope is that for other complex systems, such indicators may also identify the approach of a tipping point and a regime shift or transition (Scheffer et al. 2009). For policy making, this is a relevant perspective, as it helps in understanding what a transition or regime shift is, and has implications for policy development. A transition implies a large-scale restructuring of a system that is composed of many interacting parts. As such, the energy system and our economy at large are examples of complex systems where billions of actors are involved, and a large number of stakeholders such as companies and countries are influencing each other. The transformation from, for example, a fossil fuel-based economy towards a sustainable energy system requires that many actors that depend on each other have to simultaneously change their behaviour. An analogy with the logistic process illustrated in Fig. 4.2 can be made. w.jager@rug.nl 4 Policy Making and Modelling in a Complex World 61 Imagine a move from the lower stable situation x = 0.5 at r = 3.3 to the upper stable situation x = 0.8. This could be achieved by increasing the value of r, moving towards the more turbulent regime of the system and then reducing r again, allowing the new state to be settled into. This implies that moving from one stable regime towards another stable regime may require a period of turbulence where the transition can happen. Something like a period of turbulence demarcating regime shifts is what seems to have occurred during many transitions in the history of the world. 4.3 Two Common Mistakes in Managing Complex Systems Turbulent stages in social systems are usually experienced as gruesome by policy- makers and managers. Most of them prefer to have grip on a situation, and try to develop and communicate a clear perspective on how their actions will affect future outcomes. Especially in communicating the rationale of their decisions to the out- side world, the complex nature of social systems is often lost. It is neither possible nor particularly useful to try and list all of the “mistakes” that policymakers might make in the face of complex systems, but two of the ways in which systems are oversimplified are quantification and compartmentalisation. Quantification implies that policy is biased towards those attributes of a system that are easy to quantify. Hence, it comes as no surprise that economic outcomes, in terms of money, are often the dominating criteria in evaluating policy. Often, this results in choosing a solution that will result in the best financial economic outcome. Whereas non-quantifiable outcomes are often acknowledged, usually the bottom line is that “we obviously have to select the most economical viable option” because “money can be spent only once”. In such a case, many other complex and qualitative outcomes might be undervalued or even ignored since the complex system has been reduced to easily measurable quantities. In many situations, this causes resistance to policies, because the non-quantifiable outcomes often have an important impact on the quality of life of people. An example would be the recent earthquakes in the north of the Netherlands due to the extraction of natural gas, where the policy perspective was mainly focussing on compensating the costs of damage to housing, whereas the population experienced a loss of quality of life due to fear and feelings of unfair treatment by the government, qualities that are hard to quantify and were undervalued in the discussion. The more complex a system is, the more appealing it seems to be to get a grip on the decision context by quantifying the problem, often in economical terms. Hence, in many complex problems, e.g. related to investments in sustainable energy, the discussion revolves around returns on investment, whereas other relevant qualities, whereas being acknowledged, lose importance because they cannot be included in the complicated calculations. Further, the ability to encapsulate and manipulate number-based representations in mathematics may give such exercises an appearance of being scientific and hence reinforce the impression that the situation is under control. However, what has happened here is a conflation of indicators with the overall quality of the goals and outcomes themselves. Indicators may well be w.jager@rug.nl 62 W. Jager and B. Edmonds useful to help judge goals and outcomes; but in complex situations, it is rare that such a judgement can be reduced to such simple dimensions. Compartimentalization is a second response of many policymakers in trying to simplify complex social systems. This is a strategy whereby a system or organi- sation is split into different parts that act (to a large extent) independently of each other as separated entities, with their own goals and internal structures. As a conse- quence, the policy/management organization will follow the structure of its division into parts. Being responsible for one part of the system implies that a bias emerges towards optimizing the performance of the own part. This is further stimulated by rewarding managers for the performance of the subsystem they are responsible for, independently of the others. However, this approach makes it difficult to account for spillover effects towards other parts of the system, particularly when the outcomes in related parts of the system are more difficult to quantify. An example would be the savings on health care concerning psychiatric care. Reducing the number of maxi- mum number of consults being covered by health insurance resulted in a significant financial savings in health care nationally. However, as a result, more people in need of psychiatric help could not afford this help, and, as a consequence, may have contributed to an increase in problems such as street crime, annoyance, and deviant behaviour. Because these developments are often qualitative in nature, hard num- bers are not available, and hence these effects are more being debated than actually being included in policy development. Interestingly, due to this compartmentalisa- tion, the direct financial savings due to the reduction of the insurance conditions may be surpassed by the additional costs made in various other parts as the system such as policing, costs of crime, and increased need for crisis intervention. Thus, the problems of quantification and compartmentalisation can exacerbate each other: A quantitative approach may facilitate compartmentalisation since it makes measure- ment of each compartment easier and if one takes simple indicates as one’s goals, then it is tempting to reduce institutional structures to separate compartments that can concentrate on these narrow targets. We coin the term “Excellification”—after Microsoft Excel—to express the tendency to use quantitative measurements and compartmentalise systems in getting a grip on systems. Whereas we are absolutely convinced of the value of using measurements in developing and evaluating policy/management, it is our stance that policy making in complex systems is requiring a deeper level of understanding the processes that guide the developments in the system at hand. When trying to steer policy in the face of a complex and dynamic situation, there are essentially two kinds of strategies being used in developing this understanding: instrumental and representational. We look at these next, before we discuss how agent-based modelling may contribute to understanding and policy making in complex systems. 4.4 Complexity and Policy Making An instrumental approach is where one chooses between a set of possible policies and then evaluates them according to some assessment of their past effectiveness. w.jager@rug.nl 4 Policy Making and Modelling in a Complex World 63 Fig. 4.3 An illustration of the instrumental approach Choose one and put it into effect (work out what to do) actionindicators Strategy 1 Strategy 2 etc. Strategy 3 Evaluate how successful strategy was In future iterations, one then adapts and/or changes the chosen policy in the light of its track record. The idea is illustrated in Fig. 4.3. This can be a highly adaptive approach, reacting rapidly in the light of the current effectiveness of different strate- gies. No initial knowledge is needed for this approach, but rather the better strategies develop over time, given feedback from the environment. Maybe, the purest form of this is the “blind variation and selective retention” of Campbell (1960), where new variants of strategies are produced (essentially) at random, and those that work badly are eliminated, as in biological evolution. The instrumental approach works better when: there is a sufficient range of strategies to choose between, there is an effective assessment of their efficacy, and the iterative cycle of trial and assessment is rapid and repeated over a substantial period of time. The instrumental approach is often used by practitioners who might develop a sophisticated “menu” of what strategies seem to work under different sets of circumstances. An example of this might be adjusting the level of some policy instrument such as the level of tolls that are designed to reduce congestion on certain roads. If there is still too much congestion, the toll might be raised; if there is too little usage, the toll might be progressively lowered. The representational approach is a little more complicated. One has a series of “models” of the environment. The models are assessed by their ability to pre- dict/mirror observed aspects of the environment. The best model is then used to evaluate possible actions in terms of an evaluation of the predicted outcomes from those actions and the one with the best outcome chosen to enact. Thus, there are two “loops” involved: One in terms of working out predictions of the models and seeing which best predicts what is observed, and the second is a loop of evaluating possible actions using the best model to determine which action to deploy. Figure 4.4 illus- trates this approach. The task of developing, evaluating, and changing the models is an expensive one, so the predictive power of these models needs to be weighed against this cost. Also, the time taken to develop the models means that this approach is often slower to adapt to changes in the environment than a corresponding instru- mental approach. However, one significant advantage of this approach is that, as a result of the models, one might have a good idea of why certain things were hap- pening in the environment, and hence know which models might be more helpful, w.jager@rug.nl 64 W. Jager and B. Edmonds Fig. 4.4 An illustration of the representational approach Choose one, work out predictions of effects of possible actions actionperception Model 1 Model 2 etc. Model 3 Evaluate whether predicitons were accurate as well as allowing for the development of longer term strategies addressing the root causes of such change. The representational approach is the one generally followed by scientists because they are interested in understanding what is happening. An example of the representational approach might be the use of epidemiolog- ical models to predict the spread of an animal disease, given different contain- ment/mitigation strategies to deal with the crisis. The models are used to predict the outcomes of various strategies, which can inform the choice of strategy. This prediction can be useful even if the models are being improved, at the same time, due to the new data coming in because of the events. Of course, these two approaches are frequently mixed. For example, representa- tional models might be used to constrain which strategies are considered within an otherwise instrumental approach (even if the representational models themselves are not very good at prediction). If a central bank is considering what interest rate to set, there is a certain amount of trial and error: thus, exactly how low one has to drop the interest rates to get an economy going might be impossible to predict, and one just has to progressively lower them until the desired effect achieved. However, some theory will also be useful: thus, one would know that dropping interest rates would not be the way to cool an over-heating economy. Thus, even very rough models with relatively poor predictive ability (such as “raising interest rates tends to reduce the volume of economic activity and lowering them increases it”) can be useful. Complexity theory is useful for the consideration of policy in two different ways. First, it can help provide representational models that might be used to constrain the range of strategies under consideration and, second, can help inform second- order considerations concerning the ways in which policy might be developed and/or adopted—the policy adaption process itself. In the following section, we first look at the nature and kinds of models so as to inform their best use within the policy modelling, and later look at how second-order considerations may inform how we might use such models. w.jager@rug.nl 4 Policy Making and Modelling in a Complex World 65 Fig. 4.5 An illustration in some of the opposing desiderata of models simplicity generality validity formality 4.4.1 Using Formal Models in Policy Making The use of models in policy making starts with the question—what the appropriate policy models are? Many models are often available because (1) improving mod- els following the representational approach will yield series of models that further improve the representation of the process in terms of cause–effect relations, and (2) sometimes more extended models are required for explaining a process, whereas often simpler models are used to represent a particular behaviour. Realising that many models are often available, we still have to keep in mind that any model is an abstraction. A useful model is necessarily simpler than what it represents, so that much is left out—abstracted away. However, the decision as to what needs to be represented in a model and what can be safely left out is a difficult one. Some models will be useful in some circumstances and useless in others. Also, a model that is useful for one purpose may well be useless for another. Many of the problems associated with the use of models to aid the formulation and steering of policy derive from an assumption that a model will have value per se, independent of context and purpose. One of the things that affect the uses to which models can be put is the compromise that went into the formulation of the models. Figure 4.5 illustrates some of these tensions in a simple way. These illustrated desiderata refer to a model that is being used. Simplicity is how simple the model is, the extent to which the model itself can be completely understood. Analytically solvable mathematical models, most statistical models, and abstract simulation models are at the relatively simple end of the spectrum. Clearly, a simple model has many advantages in terms of using the model, checking it for bugs and mistakes (Galán et al. 2009), and communicating it. However, when modelling complex systems, such as what policymakers face, such simplicity may not be worth it if gaining it means a loss of other desirable properties. Generality is the extent of the model scope: How many different kinds of situations could the model be usefully applied. Clearly, some level of generality is desirable; otherwise one could only apply w.jager@rug.nl 66 W. Jager and B. Edmonds the model in a single situation. However, all policy models will not be completely general—there will always be assumptions used in their construction, which limit their generality. Authors are often rather lax about making the scope of their models clear—often implying a greater level of generality that can be substantiated. Finally, validity means the extent to which the model outcomes match what is observed to occur—it is what is established in the process of model validation. This might be as close a match as a point forecast, or as loose as projecting qualitative aspects of possible outcomes. What policymakers want, above all, is validity, with generality (so they do not have to keep going back to the modellers) and simplicity (so there is an accessible narrative to build support for any associated policy) coming after this. Simplicity and generality are nice if you can get them, but one cannot assume that these are achiev- able (Edmonds 2013). Validity should be an overwhelming priority for modellers; otherwise, they are not doing any sort of empirical science. However, they often put this off into the future, preferring the attractions of the apparent generality offered by analogical models (Edmonds 2001, 2010). Formality is the degree to which a model is built in a precise language or system. A system of equations or a computer simulation is formal, vague, but intuitive ideas expressed in natural language are informal. It must be remembered that formality for those in the policy world is not a virtue but more of a problem. They may be convinced it is necessary (to provide the backing of “science”), but it means that the model is inevitably somewhat opaque and not entirely under their control. This is the nub of the relationship between modellers and the policy world—if the policy side did not feel any need for the formality, then they would have no need of modellers—they are already skilled at making decisions using informal methods. For the modellers, the situation is reverse. Formality is at the root of modelling, so that they can replicate their results and so that the model can be unambiguously passed to other researchers for examination, critique, and further development (Edmonds 2000). For this reason, we will discuss formality a bit and analyse its nature and consequences. Two dimensions of formality can be usefully distinguished here, these are: a. The extent to which the referents of the representation are constrained (“specificity of reference”). b. The extent to which the ways in which instantiations of the representation can be manipulated are constrained (“specificity of manipulation”). For example, an analogy expressed in natural language has a low specificity of reference since, what its parts refer to are reconstructed by each hearer in each situation. For example, the phrase “a tidal wave of crime” implies that concerted and highly coordinated action is needed in order to prevent people being engulfed, but the level of danger and what (if anything) is necessary to do must be determined by each listener. In contrast to this is a detailed description where what it refers to is severely limited by its content, e.g. “Recorded burglaries in London rose by 15 % compared to the previous year”. Data are characterised by a high specificity of reference, since what it refers to is very precise, but has a low specificity of manipulation because there are few constraints in what one can do with it. w.jager@rug.nl 4 Policy Making and Modelling in a Complex World 67 A system of mathematics or computer code has a high specificity of manipulation since the ways these can be manipulated are determined by precise rules—what one person infers from them can be exactly replicated by another. Thus, all formal models (the ones we are mostly concentrating on here) have a high specificity of manipulation, but not necessarily a high specificity of representation. A piece of natural language that can be used to draw inferences in many different ways, only limited by the manipulators’ imagination and linguistic ability, has a low specificity of manipulation. One might get the impression that any “scientific” model expressed in mathematics must be formal in both ways. However, just because a representation has high specificity of manipulation, it does not mean that the meaning of its parts in terms of what it represents is well determined. Many simulations, for example, do not represent anything we observe directly, but are rather explorations of ideas. We, as intelligent interpreters, may mentally fill in what it might refer to in any particular context but these “mappings” to reality are not well defined. Such models are more in the nature of an analogy, albeit one in formal form—they are not testable in a scientific manner since it is not clear as to precisely what they represent. Whilst it may be obvious when a system of mathematics is very abstract and not directly connected with what is observed, simulations (especially agent-based simulations) can give a false impression of their applicability because they are readily interpretable (but informally). This does not mean they are useless for all purposes. For example, Schelling’s abstract simulation of racial segregation did not have any direct referents in terms of anything measurable,3 but it was an effective counterexample that can show that an assumption that segregation must be caused by strong racial prejudice was unsound. Thus, such “analogical models” (those with low specificity of reference) can give useful insights—they can inform thought, but cannot give reliable forecasts or explanations as to what is observed. In practice, a variety of models are used by modellers in the consideration of any issue, including: informal analogies or stories that summarise understanding and are used as a rough guide to formal manipulation, data models that abstract and represent the situation being modelled via observation and measurement, the simulation or mathematical model that is used to infer something about outcomes from initial situations, representations of the outcomes in terms of summary measures and graphs, and the interpretations of the results in terms of the target situation. When considering very complex situations, it is inevitable that more models will become involved, abstracting different aspects of the target situation in different ways and “staging” abstraction so that the meaning and reference can be maintained. However, good practice in terms of maintaining “clusters” of highly related models has yet to be established in the modelling community, so that a policymaker might well be bewildered by different models (using different assumptions) giving apparently conflicting results. However, the response to this should not be to reject this variety, and enforce comforting (but ultimately illusory) consistency of outcomes, but accept 3 Subsequent elaborations of this model have tried to make the relationship to what is observed more direct, but the original model, however visually suggestive, was not related to any data. w.jager@rug.nl 68 W. Jager and B. Edmonds that it is useful to have different viewpoints from models as much as it is to have different viewpoints from experts. It is the job of policymakers to use their experience and judgement in assessing and combining these views of reality. Of course, equally it is the job of the modellers to understand and explain why models appear to contradict each other and the significance of this as much as they can. A model that looks scientific (e.g. is composed of equations, hence quantified) might well inspire more confidence than one that does not. In fact, the formality of models is very much a two-edged sword, giving advantages and disadvantages in ways that are not immediately obvious to a nonmodeller. We will start with the disadvantages and then consider the advantages. Most formal models will be able to output series of numbers composed of mea- sures on the outcomes of the model. However, just because numbers are by their nature precise,4 does not mean that this precision is representative of the certainty to which these outcomes will map to observed outcomes. Thus, numerical outcomes can give a very false sense of security, and lead those involved in policy to falsely think that prediction of such values is possible. Although many forecasters now will add indications of uncertainty “around” forecasts, this can still be deeply misleading as it still implies that there is a central tendency about which future outcomes will gravitate.5 Many modellers are now reluctant to make such predictions because they know how misleading these can be. This is, understandably, frustrating for those involved in policy, whose response might be, “I know its complex, but we do not have the time/money to develop a more sophisticated model so just give me your ‘best guess”’. This attitude implies that some prediction is better than none, and that the reliability of a prediction is monotonic to the amount of effort one puts in. It seems that many imagine that the reliability of a prediction increases with effort, albeit unevenly—so a prediction with a small amount of effort will be better than none at all. Unfortunately, this is far from the case, and a prediction based on a “quick and dirty” method may be more misleading than helpful and merely give a false sense of security. One of the consequences of the complexity of social phenomena is that the pre- diction of policy matters is hard, rare, and only obtained as a result of the most specific and pragmatic kind of modelling developed over relatively long periods of time.6 It is more likely that a model is appropriate for establishing and understanding candidate explanations of what is happening, which will inform policy making in a less exact manner than prediction, being part of the mix of factors that a policymaker will take into account when deciding action. It is common for policy people to want a prediction of the impact of possible interventions “however rough”, rather than settle for some level of understanding of what is happening. However, this can be 4 Even if, as in statistics, they are being precise about variation and levels of uncertainty of other numbers. 5 This apparent central tendency might be merely the result of the way data are extracted from the model and the assumptions built into the model rather than anything that represents the fundamental behaviour being modelled. 6 For an account of actual forecasting and its reality, see Silver (2012). w.jager@rug.nl 4 Policy Making and Modelling in a Complex World 69 illusory—if one really wanted a prediction “however rough”, one would settle for a random prediction7 dressed up as a complicated “black box” model. If we are wiser, we should accept the complexity of what we are dealing and reject models that give us ill-founded predictions. Maybe a better approach is to use the modelling to inform the researchers about the kinds of process that might emerge from a situation—showing them possible “trajectories” that they would not otherwise have imagined. Using visualisations of these trajectories and the critical indicators clarifies the complex decision context for policymakers. In this way, the burden of uncertainty and decision making remains with the policymakers and not the researchers, but they will be more intelligently informed about the complexity of what is currently happening, allowing them to “drive” decision making better. As we have discussed above, one feature of complex systems is that they can result in completely unexpected outcomes, where due to the relevant interactions in the system, a new kind of process has developed resulting in qualitatively different results. It is for this reason that complex models of these systems do not give prob- abilities (since these may be meaningless, or worse be downright misleading) but rather trace some (but not all) of the possible outcomes. This is useful as one can then be as prepared as possible for such outcomes, which otherwise would not have been thought of. On the positive side, the use of formal modelling techniques can be very helpful for integrating different kinds of understanding and evidence into a more “well- rounded” assessment of options. The formality of the models means that it can be shared without ambiguity or misunderstanding between experts in different domains. This contrasts with communication using natural language where, inevitably, people have different assumptions, different meanings, and different inferences for key terms and systems. This ability to integrate different kinds of expertise turns out to be especially useful in the technique we will discuss next—agent-based simulation. 4.4.2 The Use of Agent-Based Models to Aid Policy Formation In recent years, agent-based simulation has gained momentum as a tool allowing the computer to simulate the interactions between a great number of agents. An agent- based simulation implies that individuals can be represented as separate computer models that capture their motives and behaviour. Letting these so-called agents in- teract though a network, and confront them with changing circumstances, creates an artificial environment where complex and highly dynamic processes can be stud- ied. Because agent-based models address the interactions between many different agents, they offer a very suitable tool to represent and recreate the complexities in so- cial systems. Hence, agent-based modeling has become an influential methodology 7 Or other null model, such as “what happened last time” or “no change”. w.jager@rug.nl 70 W. Jager and B. Edmonds to study a variety of social systems, ranging from ant colonies to aspects of human society. In the context of agent-based simulation of human behaviour, one of the challenges is connecting the knowledge from behavioural sciences in agent-based models that can be used to model behaviour in some kind of environment. These mod- elled environments may differ largely, and may reflect different (inter)disciplinary fields. Examples of environments where agents can operate in are, e.g. financial markets, agricultural settings, the introduction of new technologies in markets, and transportation systems, just to name a few. A key advantage here is that a model creates a common formal language for different disciplines to communicate. This is important, as it allows for speaking the same language in targeting issues that are interdisciplinary by nature. Rather than taking information from social scientists as an interesting qualitative advice, it becomes possible to actually simulate what the behaviour dynamical effects of policies are. This is, in our view, an important step in addressing interdisciplinary policy issues in an effective way. An additional advantage of social simulation is that formalizing theory and empirical data in mod- els requires researchers to be exact in the assumptions, which, in turn, may result in specific research questions for field and/or lab experiments. Hence, social simulation is a tool that both stimulates the interaction between scientific disciplines, and may stimulate theory development/specification within the behavioural sciences. An increasing number of agent-based models is being used in a policy context. A recent inventory on the SIMSOC mailing list by Nigel Gilbert8 resulted in a list of modelling projects that in some way were related to actual policy making. Topics included energy systems, littering, water management, crowd dynamics, financial crisis, health management, deforestation, industrial clustering, biogas use, military interventions, diffusion of electric cars, organization of an emergency centre, natural park management, postal service organization, urban design, introduction of renew- able technology, and vaccination programmes. Whereas some models were actually being used by policymakers, in most instances, the models were being used to in- form policy makers about the complexities in the system they were interacting with. The basic idea is that a better understanding of the complex dynamics of the system contributes to understanding how to manage these systems, even if they are unpre- dictable by nature. Here, a comparison can be made with sailing as a managerial process. Sailing can be seen as a managerial challenge in using different forces that con- stantly change and interact in order to move the ship to a certain destination. In stable and calm weather conditions, it is quite well possible to set the sails in a certain posi- tion and fix the rudder, and make an accurate prediction where of the course the boat will follow. The situation becomes different when you enter more turbulent stages in the system, and strong and variable winds, in combination with bigger waves and streams, requiring the sailor to be very adaptive to the circumstances. A small deviation from the course, due to a gush or a wave, may alter the angle of the wind 8 See mailing list SIMSOC@JISCMAIL.AC.UK. Mail distributed by Nigel Gilbert on December 14, 2013, subject: ABMs in action: second summary. w.jager@rug.nl 4 Policy Making and Modelling in a Complex World 71 in the sail, which may give rise to further deviations of the course. This is typically a feedback process, and obviously an experienced sailor is well aware of all these dynamics, and, as a consequence, the sailor responds very adaptive to these small disturbances, yet keeps the long-term outcome—the destination port—also in mind. The social systems that we are dealing with, in transitions, are way more com- plex than the sailing example. Yet, the underlying rational is the same: the better we learn to understand the dynamics of change, the better we will be capable of coping with turbulences in the process, whilst keeping the long-term goals in focus. Hence, policy aims such that the transition towards a sustainable energy future provides a reasonably clear picture of the direction we are aiming for, but the turbulences in the process towards this future are not well known. Where the sailor has a deep under- standing of the dynamics that govern the behaviour of his boat, for policymakers, this understanding is often limited, as the opening example demonstrated. Using agent-based models for policy would contribute to a better understand- ing and management of social complex phenomena. First, agent-based models will be useful in identifying under what conditions a social system will behave rela- tively stable (predictable) versus turbulent (unpredictable). This is critical for policy making, because in relatively stable situations, predictions can be made concern- ing the effects of policy, whereas in turbulent regimes, a more adaptive policy is recommended. Adaptive policy implies that the turbulent developments are being followed closely, and that policymakers try to block developments to grow in an undesired direction, and benefit and support beneficial developments. Second, if simulated agents are more realistic in the sense that they are equipped with differ- ent utilities/needs/preferences, the simulations will not only show what the possible behavioural developments are but also reveal the impact on a more psychologi- cal quality-of-life level. Whereas currently many policy models assess behavioural change from a more financial/economical drivers, agent-based models open a pos- sibility to strengthen policy models by including additional outcomes. Examples would be outcomes relating to the stability and support in social networks, and general satisfaction levels. Agent-based models, thus, can provide a richer and more complex representation of what may be happening within complex and highly dynamic situations, allowing for some of the real possibilities within the system to be explored. This exploration of possibilities can inform the risk analysis of policy, and help ensure that policymakers are ready for more of what the world may throw at them, for example, by having put in place custom-designed indicators that give them the soonest-possible indication that certain kinds of processes or structural changes are underway. 4.5 Conclusions The bad news for policymakers is that predictive models perform worst exactly at the moment policymakers need them most—during turbulent stages. Yet, we observe that many policymakers, not being aware of the complex nature of the system they w.jager@rug.nl 72 W. Jager and B. Edmonds are interfering with, still have a mechanistic worldview, and base their decisions on classical predictions. This may be one of the reasons for scepticism by policymakers of any modelling approaches (see e.g. Waldherr and Wijermans 2013). Even nowa- days, when complexity has turned into a buzzword, many policymakers still confuse this concept with “complicatedness”, not embracing the essence and meaning of what complexity means for understanding social systems. As a consequence, still many policymakers are “Cartesian9” in their demand for better predictive models. On the other side, still many modellers working from a mechanistic perspective (e.g. linear and/or generic models), holding out the false hope of “scientifically” predic- tive models, look for more resources to incrementally improve their models, e.g. covering more variables. However, whereas it is sometimes justified to argue for the inclusion of more variables in a model, this will not contribute to a better predictive capacity of the model. As Scott Moss reports in his paper (Moss 2002), there are no reported correct real-time forecasts of the volatile clusters or the post-cluster levels in financial market indices or macroeconomic trade cycles, despite their incremental “refinement” over many years. Characteristically, they predict well in periods where nothing much changes, but miss all the “turning points” where structural change occurs. Even if policymakers have some understanding of the complex nature of the systems they are managing, they still often respond with “I know it is complex, but how else can I decide policy except by using the numbers I have?”, indicating that the numbers are often an important justification of decisions, even if people are aware of the uncertainties behind them. The example of the former minister in the introduction is a prototypical example of this decision making. The challenge, hence, is not in trying to convince policymakers of the value of simulation models, but providing them with a deeper level understanding of complex systems. Here, simulation models can provide an important role by creating learning experiences. But before going to simulation models, it might be important to use a strong metaphor in anchoring the core idea of managing complex systems. Sailing offers an excellent metaphor here, because many people know the basics of sailing, and understand that it deals with the management of a ship in sometimes turbulent circumstances. What is critical in this metaphor is that in more turbulent conditions, the crew should become more adaptive to the developments in the system. Agent-based simulation is increasingly being used as a modelling tool to explore the possibilities and potential impacts of policy making in complex systems. They are inherently possibilistic rather than probabilistic. However, the models being used are usually not very accessible for policymakers. Also, in the context of education, not many models are available that allow for an easy access to experiencing policy making in complex systems. In Chap. 13 of this book, Jager and Van der Vegt suggest using based gaming as a promising venue to make agent-based models more 9 Descartes’ mechanistic worldview implies that the universe works like a clockwork, and prediction is possible when one has knowledge of all the wheels, gears, and levers of the clockwork. In policy this translates as the viable society. w.jager@rug.nl 4 Policy Making and Modelling in a Complex World 73 accessible in education and practical policy settings. A setting where valid games are being used to increase our understanding of the processes in complex management issues is expected to contribute to an improvement of the policy-making process in complex systems. Acknowledgments This chapter has been written in the context of the eGovPoliNet project. More information can be found on http://www.policy-community.eu/. References Boettiger C, Hastings A (2012) Quantifying limits to detection of early warning for critical transitions. J R Soc Interface 9(75):2527–2539 Campbell DT (1960) Blind variation and selective retention in creative thought as in other knowledge processes. Psychol Rev 67:380–400 Dai L, Vorselen D et al (2012) Generic indicators for loss of resilience before a tipping point leading to population collapse. Science 336(6085):1175–1177 Dakos V, Carpenter RA et al (2012) Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS ONE 7(7) e41010 Edmonds B (2000) The purpose and place of formal systems in the development of science. CPM report 00–75, MMU, UK (http://cfpm.org/cpmrep75.html) Edmonds B (2001) The use of models—making MABS actually work. In: Moss S, Davidsson P (eds) Multi agent based simulation. Lecture Notes in Artificial Intelligence 1979. Springer, Berlin, pp 15–32 Edmonds B (2010) Bootstrapping knowledge about social phenomena using simulation models. J Artif Soc Soc Simul 13(1):8 (http://jasss.soc.surrey.ac.uk/13/1/8.html) Edmonds B (2013) Complexity and context-dependency. Found Sci 18(4):745–755. doi:10.1007/s10699-012-9303-x Galán JM, Izquierdo LR, Izquierdo SS, Santos JI, del Olmo R, López-Paredes A, Edmonds B (2009) Errors and artefacts in agent-based modelling. J Artif Soc Soc Simul 12(1):1 (http://jasss.soc.surrey.ac.uk/12/1/1.html) Heisenberg W (1927) Ueber den anschaulichenInhalt der quantentheoretischen. Kinematik and Mechanik Zeitschriftfür Physik 43:172–198. English translation in (Wheeler and Zurek, 1983), pp 62–84 May RM (1976) Simple mathematical models with very complicated dynamics. Nature 261(5560):459–467 Moss S (2002) Policy analysis from first principles. Proc US Natl Acad Sci 99(Suppl 3):7267–7274 Scheffer et al (2009) Early warnings of critical transitions. Nature 461:53–59 Silver N (2012) The signal and the noise: why so many predictions fail-but some don’t. Penguin, New York Waldherr A, Wijermans N (2013) Communicating social simulation models to sceptical minds. J Artif Soc Soc Simul 16(4):13 (http://jasss.soc.surrey.ac.uk/16/4/13.html) w.jager@rug.nl Chapter 5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling Erik Pruyt Abstract Starting from the state-of-the-art and recent evolutions in the field of system dynamics modeling and simulation, this chapter sketches a plausible near term future of the broader field of systems modeling and simulation. In the near term future, different systems modeling schools are expected to further integrate and accelerate the adoption of methods and techniques from related fields like policy analysis, data science, machine learning, and computer science. The resulting future state of the art of the modeling field is illustrated by three recent pilot projects. Each of these projects required further integration of different modeling and simulation approaches and related disciplines as discussed in this chapter. These examples also illustrate which gaps need to be filled in order to meet the expectations of real decision makers facing complex uncertain issues. 5.1 Introduction Many systems, issues, and grand challenges are characterized by dynamic com- plexity, i.e., intricate time evolutionary behavior, often on multiple dimensions of interest. Many dynamically complex systems and issues are relatively well known, but have persisted for a long time due to the fact that their dynamic complexity makes them hard to understand and properly manage or solve. Other complex systems and issues—especially rapidly changing systems and future grand challenges—are largely unknown and unpredictable. Most unaided human beings are notoriously bad at dealing with dynamically complex issues—whether the issues dealt with are persistent or unknown. That is, without the help of computational approaches, most human beings are unable to assess potential dynamics of complex systems and issues, and are unable to assess the appropriateness of policies to manage or address them. E. Pruyt (�) Faculty of Technology, Policy, and Management, Delft University of Technology, Delft, The Netherlands e-mail: E.Pruyt@tudelft.nl Netherlands Institute for Advanced Study, Wassenaar, The Netherlands © Springer International Publishing Switzerland 2015 75 M. Janssen et al. (eds.), Policy Practice and Digital Science, Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_5 w.jager@rug.nl 76 E. Pruyt Modeling and simulation is a field that develops and applies computational meth- ods to study complex systems and solve problems related to complex issues. Over the past half century, multiple modeling methods for simulating such issues and for advising decision makers facing them have emerged or have been further devel- oped. Examples include system dynamics (SD) modeling, discrete event simulation (DES), multi-actor systems modeling (MAS), agent-based modeling (ABM), and complex adaptive systems modeling (CAS). All too often, these developments have taken place in distinct fields, such as the SD field or the ABM field, developing into separate “schools,” each ascribing dynamic complexity to the complex underlying mechanisms they focus on, such as feedback effects and accumulation effects in SD or heterogenous actor-specific (inter)actions in ABM. The isolated development within separate traditions has limited the potential to learn across fields and advance faster and more effectively towards the shared goal of understanding complex systems and supporting decision makers facing complex issues. Recent evolutions in modeling and simulation together with the recent explosive growth in computational power, data, social media, and other evolutions in computer science have created new opportunities for model-based analysis and decision mak- ing. These internal and external evolutions are likely to break through silos of old, open up new opportunities for social simulation and model-based decision making, and stir up the broader field of systems modeling and simulation. Today, different modeling approaches are already used in parallel, in series, and in mixed form, and several hybrid approaches are emerging. But not only are different modeling tradi- tions being mixed and matched in multiple ways, modeling and simulation fields have also started to adopt—or have accelerated their adoption of—useful methods and techniques from other disciplines including operations research, policy analysis, data analytics, machine learning, and computer science. The field of modeling and simulation is consequently turning into an interdisciplinary field in which various modeling schools and related disciplines are gradually being integrated. In prac- tice, the blending process and the adoption of methodological innovations have just started. Although some ways to integrate systems modeling methods and many in- novations have been demonstrated, further integration and massive adoption are still awaited. Moreover, other multi-methods and potential innovations are still in an experimental phase or are yet to be demonstrated and adopted. In this chapter, some of these developments will be discussed, a picture of the near future state of the art of modeling and simulation is drawn, and a few examples of integrated systems modeling are briefly discussed. The SD method is used to illustrate these developments. Starting with a short introduction to the traditional SD method in Sect. 5.2, some recent and current innovations in SD are discussed in Sect. 5.3, resulting in a picture of the state of modeling and simulation in Sect. 5.4. A few examples are then briefly discussed in Sect. 5.5 to illustrate what these developments could result in and what the future state-of-the-art of systems modeling and simulation could look like. Finally, conclusions are drawn in Sect. 5.6. w.jager@rug.nl 5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling 77 5.2 System Dynamics Modeling and Simulation of Old System dynamics was first developed in the second half of the 1950s by Jay W. Forrester and was further developed into a consistent method built on specific method- ological choices1. It is a method for modeling and simulating dynamically complex systems or issues characterized by feedback effects and accumulation effects. Feed- back means that the present and future of issues or systems, depend—through a chain of causal relations—on their own past. In SD models, system boundaries are set broadly enough to include all important feedback effects and generative mecha- nisms. Accumulation relates not only to building up real stocks—of people, items, (infra)structures, etc.,—but also to building up mental or other states. In SD mod- els, stock variables and the underlying integral equations are used to group largely homogenous persons/items/. . . and keep track of their aggregated dynamics over time. Together, feedback and accumulation effects generate dynamically complex behavior both inside SD models and—so it is assumed in SD—in real systems. Other important characteristic of SD are (i) the reliance on relatively enduring conceptual systems representations in people’s minds, aka mental models (Doyle and Ford 1999, p. 414), as prime source of “rich” information (Forrester 1961; Doyle and Ford 1998); (ii) the use of causal loop diagrams and stock-flow diagrams to represent feedback and accumulation effects (Lane 2000); (iii) the use of credibility and fitness for purpose as main criteria for model validation (Barlas 1996); and (iv) the interpretation of simulation runs in terms of general behavior patterns, aka modes of behavior (Meadows and Robinson 1985). In SD, the behavior of a system is to be explained by a dynamic hypothesis, i.e., a causal theory for the behavior (Lane 2000; Sterman 2000). This causal theory is formalized as a model that can be simulated to generate dynamic behavior. Simulating the model thus allows one to explore the link between the hypothesized system structure and the time evolutionary behavior arising out of it (Lane 2000). Not surprisingly, these characteristics make SD particularly useful for dealing with complex systems or issues that are characterized by important system feedback effects and accumulation effects. SD modeling is mostly used to model core system structures or core structures underlying issues, to simulate their resulting behavior, and to study the link between the underlying causal structure of issues and models and the resulting behavior. SD models, which are mostly relatively small and manageable, thus allow for experimentation in a virtual laboratory. As a consequence, SD models are also extremely useful for model-based policy analysis, for designing adaptive policies (i.e., policies that automatically adapt to the circumstances), and for testing their policy robustness (i.e., whether they perform well enough across a large variety of circumstances). 1 See Forrester (1991, 2007), Sterman (2007) for accounts of the inception of the SD field. See Sterman (2000), Pruyt (2013) for introductions to SD. And see Forrester (1961, 1969), Homer (2012) for well-known examples of traditional SD. w.jager@rug.nl 78 E. Pruyt In terms of application domains, SD is used for studying many complex social– technical systems and solving policy problems in many application domains, for example, in health policy, resource policy, energy policy, environmental policy, housing policy, education policy, innovation policy, social–economic policy, and other public policy domains. But it is also used for studying all sorts of business dynamics problems, for strategic planning, for solving supply chain problems, etc. At the inception of the SD method, SD models were almost entirely continuous, i.e., systems of differential equations, but over time more and more discrete and other noncontinuous elements crept in. Other evolutionary adaptations in line with ideas from the earliest days of the field, like the use of Group Model Building to elicit mental models of groups of stakeholders (Vennix 1996) or the use of SD models as engines for serious games, were also readily adopted by almost the entire field. But slightly more revolutionary innovations were not as easily and massively adopted. In other words, the identity and appearance of traditional SD was well established by the mid-1980s and does—at first sight—not seem to have changed fundamentally since then. 5.3 Recent Innovations and Expected Evolutions 5.3.1 Recent and Current Innovations Looking in somewhat more detail at innovations within the SD field and its adop- tion of innovations from other fields shows that many—often seemingly more revolutionary—innovations have been introduced and demonstrated, but that they have not been massively adopted yet. For instance, in terms of quantitative modeling, system dynamicists have invested in spatially specific SD modeling (Ruth and Pieper 1994; Struben 2005; BenDor and Kaza 2012), individual agent-based SD modeling as well as mixed and hybrid ABM- SD modeling (Castillo and Saysal 2005; Osgood 2009; Feola et al. 2012; Rahmandad and Sterman 2008), and micro–macro modeling (Fallah-Fini et al. 2014). Examples of recent developments in simulation setup and execution include model calibration and bootstrapping (Oliva 2003; Dogan 2007), different types of sampling (Fiddaman 2002; Ford 1990; Clemson et al. 1995; Islam and Pruyt 2014), multi-model and multi- method simulation (Pruyt and Kwakkel 2014; Moorlag 2014), and different types of optimization approaches used for a variety of purposes (Coyle 1985; Miller 1998; Coyle 1999; Graham and Ariza 1998; Hamarat et al. 2013, 2014). Recent innovations in model testing, analysis, and visualization of model outputs in SD include the development and application of new methods for sensitivity and uncertainty analysis (Hearne 2010; Eker et al. 2014), formal model analysis methods to study the link between structure and behavior (Kampmann and Oliva 2008, 2009; Saleh et al. 2010), methods for testing policy robustness across wide ranges of uncertainties (Lempert et al. 2003), statistical packages and screening techniques (Ford and Flynn 2005; Taylor et al. 2010), pattern testing and time series classification techniques w.jager@rug.nl 5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling 79 (Yücel and Barlas 2011; Yücel 2012; Sucullu and Yücel 2014; Islam and Pruyt 2014), and machine learning techniques (Pruyt et al. 2013; Kwakkel et al. 2014; Pruyt et al. 2014c). These methods and techniques can be used together with SD models to identify root causes of problems, to identify adaptive policies that properly address these root causes, to test and optimize the effectiveness of policies across wide ranges of assumptions (i.e., policy robustness), etc. From this perspective, these methods and techniques are actually just evolutionary innovations in line with early SD ideas. And large-scale adoption of the aforementioned innovations would allow the SD field, and by extension the larger systems modeling field, to move from “experiential art” to “computational science.” Most of the aforementioned innovations are actually integrated in particular SD approaches like in exploratory system dynamics modelling and analysis (ESDMA), which is an SD approach for studying dynamic complexity under deep uncertainty. Deep uncertainty could be defined as a situation in which analysts do not know or cannot agree on (i) an underlying model, (ii) probability distributions of key variables and parameters, and/or (iii) the value of alternative outcomes (Lempert et al. 2003). It is often encountered in situations characterized by either too little information or too much information (e.g., conflicting information or different worldviews). ESDMA is the combination of exploratory modeling and analysis (EMA), aka robust decision making, developed during the past two decades (Bankes 1993; Lempert et al. 2000; Bankes 2002; Lempert et al. 2006) and SD modeling. EMA is a research methodology for developing and using models to support decision making under deep uncertainty. It is not a modeling method, in spite of the fact that it requires computational models. EMA can be useful when relevant information that can be exploited by building computational models exists, but this information is insufficient to specify a single model that accurately describes system behavior (Kwakkel and Pruyt 2013a). In such situations, it is better to construct and use ensembles of plausible models since ensembles of models can capture more of the un/available information than any individual model (Bankes 2002). Ensembles of models can then be used to deal with model uncertainty, different perspectives, value diversity, inconsistent information, etc.—in short, with deep uncertainty.2 In EMA (and thus in ESDMA), the influence of a plethora of uncertainties, includ- ing method and model uncertainty, are systematically assessed and used to design policies: sampling and multi-model/multi-method simulation are used to generate ensembles of simulation runs to which time series classification and machine learning techniques are applied for generating insights. Multi-objective robust optimization (Hamarat et al. 2013, 2014) is used to identify policy levers and define policy triggers, and by doing so, support the design of adaptive robust policies. And regret-based approaches are used to test policy robustness across large ensembles of plausible runs (Lempert et al. 2003). EMA and ESDMA can be performed with TU Delft’s 2 For ESDMA, see among else Pruyt and Hamarat (2010), Logtens et al. (2012), Pruyt et al. (2013), Kwakkel and Pruyt (2013a, b), Kwakkel et al. (2013), Pruyt and Kwakkel (2014). w.jager@rug.nl 80 E. Pruyt EMA workbench software, which is an open source tool3 that integrates multi- method, multi-model, multi-policy simulation with data management, visualization, and analysis. The latter is just one of the recent innovations in modeling and simulation software and platforms: online modeling and simulation platforms, online flight simulator and gaming platforms, and packages for making hybrid models have been developed too. And modeling and simulation across platforms will also become reality soon: the eXtensible Model Interchange LanguagE (XMILE) project (Diker and Allen 2005; Eberlein and Chichakly 2013) aims at facilitating the storage, sharing, and combination of simulation models and parts thereof across software packages and across modeling schools and may ease the interconnection with (real-time) databases, statistical and analytical software packages, and organizational information and com- munication technology (ICT) infrastructures. Note that this is already possible today with scripting languages and software packages with scripting capabilities like the aforementioned EMA workbench. 5.3.2 Current and Expected Evolutions Three current evolutions are expected to further reinforce this shift from “experiential art” to “computational science.” The first evolution relates to the development of “smarter” methods, techniques, and tools (i.e., methods, techniques, and tools that provide more insights and deeper understanding at reduced computational cost). Similar to the development of formal model analysis techniques that smartened the traditional SD approach, new meth- ods, techniques, and tools are currently being developed to smarten modeling and simulation approaches that rely on “brute force” sampling, for example, adaptive output-oriented sampling to span the space of possible dynamics (Islam and Pruyt 2014) or smarter machine learning techniques (Pruyt et al. 2013; Kwakkel et al. 2014; Pruyt et al. 2014c) and time series classification techniques (Yücel and Barlas 2011; Yücel 2012; Sucullu and Yücel 2014; Islam and Pruyt 2014), and (multi-objective) robust optimization techniques (Hamarat et al. 2013, 2014). Partly related to the previous evolution are developments relates to “big data,” data management, and data science. Although traditional SD modeling is sometimes called data-poor modeling, it does not mean it is, nor should be. SD software packages allow one to get data from, and write simulation runs to, databases. Moreover, data are also used in SD to calibrate parameters or bootstrap parameter ranges. But more could be done, especially in the era of “big data.” Big data simply refers here to much more data than was until recently manageable. Big data requires data science techniques to make it manageable and useful. Data science may be used in 3 The EMA workbench can be downloaded for free from http://simulation.tbm.tudelft.nl/ ema-workbench/contents.html w.jager@rug.nl http://simulation.tbm.tudelft.nl/ema-workbench/contents.html http://simulation.tbm.tudelft.nl/ema-workbench/contents.html 5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling 81 modeling and simulation (i) to obtain useful inputs from data (e.g., from real-time big data sources), (ii) to analyze and interpret model-generated data (i.e., big artificial data), (iii) to compare simulated and real dynamics (i.e., for monitoring and control), and (iv) to infer parts of models from data (Pruyt et al. 2014c). Interestingly, data science techniques that are useful for obtaining useful inputs from data may also be made useful for analyzing and interpreting model-generated data, and vice versa. Online social media are interesting sources of real-world big data for modeling and simulation, both as inputs to models, to compare simulated and real dynamics, and to inform model development or model selection. There are many application domains in which the combination of data science and modeling and simulation would be beneficial. Examples, some of which are elaborated below, include policy making with regard to crime fighting, infectious diseases, cybersecurity, national safety and security, financial stress testing, energy transitions, and marketing. Another urgently needed innovation relates to model-based empowerment of de- cision makers. Although existing flight simulator and gaming platforms are useful for developing and distributing educational flight simulators and games, and interfaces can be built in SD packages, using them to develop interfaces for real-world real-time decision making and integrating them into existing ICT systems is difficult and time consuming. In many cases, companies and organizations want these capabilities in- house, even in their boardroom, instead of being dependent on analyses by external or internal analysts. The latter requires user-friendly interfaces on top of (sets of) models possibly connected to real-time data sources. These interfaces should allow for experimentation, simulation, thoroughly analysis of simulation results, adaptive robust policy design, and policy robustness testing. 5.4 Future State of Practice of Systems Modeling and Simulation These recent evolutions in modeling and simulation together with the recent explosive growth in computational power, data, social media, and other evolutions in computer science may herald the beginning of a new wave of innovation and adoption, moving the modeling and simulation field from building a single model to simultaneously simulating multiple models and uncertainties; from single method to multi-method and hybrid modeling and simulation; from modeling and simulation with sparse data to modeling and simulation with (near real-time) big data; from simulating and analyzing a few simulation runs to simulating and simultaneously analyzing well- selected ensembles of runs; from using models for intuitive policy testing to using models as instruments for designing adaptive robust policies; and from developing educational flight simulators to fully integrated decision support. For each of the modeling schools, additional adaptations could be foreseen too. In case of SD, it may for example involve a shift from developing purely endoge- nous to largely endogenous models; from fully aggregated models to sufficiently spatially explicit and heterogenous models; from qualitative participatory modeling w.jager@rug.nl 82 E. Pruyt Fig. 5.1 Picture of the state of science/future state of the art of modeling and simulation to quantitative participatory simulation; and from using SD to combining problem structuring and policy analysis tools, modeling and simulation, machine learning techniques, and (multi-objective) robust optimization. Adoption of these recent, current, and expected innovations could result in the future state of the art4 of systems modeling as displayed in Fig. 5.1. As indicated by (I) in Fig. 5.1, it will be possible to simultaneously use multiple hypotheses (i.e., simulation models from the same or different traditions or hybrids), for different goals including the search for deeper understanding and policy insights, experimentation in a virtual laboratory, future-oriented exploration, robust policy design, and robustness testing under deep uncertainty. Sets of simulation models may be used to represent different perspectives or plausible theories, to deal with methodological uncertainty, or to deal with a plethora of important characteristics (e.g., agent characteristics, feedback and accumulation effects, spatial and network effects) without necessarily having to integrate them in a single simulation model. The main advantages of using multiple models for doing so are that each of the models in the ensemble of models remains manageable and that the ensemble of simulation runs generated with the 4 Given the fact that it takes a while before innovations are adopted by software developers and practitioners, this picture of the current state of science is at the same time a plausible picture of the medium term future of the field of modeling and simulation. w.jager@rug.nl 5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling 83 ensemble of models is likely to be more diverse which allows for testing policy robustness across a wider range of plausible futures. Some of these models may be connected to real-time or near real-time data streams, and some models may even be inferred in part with smart data science tools from data sources (see (II) in Fig. 5.1). Storing the outputs of these simulation models in databases and applying data science techniques may enhance our under- standing, may generate policy insights, and may allow for testing policy robustness across large multidimensional uncertainty spaces (see (III) in Fig. 5.1). And user- friendly interfaces on top of these interconnected models may eventually empower policy makers, enabling them to really do model-based policy making. Note, however, that the integrated systems modeling approach sketched in Fig. 5.1 may only suit a limited set of goals, decision makers, and issues. Single model simulation properly serves many goals, decision makers, and issues well enough for multi-model/multi-method, data-rich, exploratory, policy-oriented approaches not to be required. However, there are most certainly goals, decision makers, and issues that do. 5.5 Examples Although all of the above is possible today, it should be noted that this is the current state of science, not the state of common practice yet. Applying all these methods and techniques to real issues is still challenging, and shows where innovations are most needed. The following examples illustrate what is possible today as well as what the most important gaps are that remain to be filled. The first example shows that relatively simple systems models simulated under deep uncertainty allow for generating useful ensembles of many simulation runs. Using methods and techniques from related disciplines to analyze the resulting arti- ficial data sets helps to generate important policy insights. And simulation of policies across the ensembles allows to test for policy robustness. This first case nevertheless shows that there are opportunities for multi-method and hybrid approaches as well as for connecting systems models to real-time data streams. The second example extends the first example towards a system-of-systems ap- proach with many simulation models generating even larger ensembles of simulation runs. Smart sampling and scenario discovery techniques are then required to reduce the resulting data sets to manageable proportions. The third example shows a recent attempt to develop a smart model-based decision-support system for dealing with another deeply uncertain issue. This ex- ample shows that it is almost possible to empower decision makers. Interfaces with advanced analytical capabilities as well as easier and better integration with existing ICT systems are required though. This example also illustrates the need for more advanced hybrid systems models as well as the need to connect systems models to real-time geo-spatial data. w.jager@rug.nl 84 E. Pruyt 5.5.1 Assessing the Risk, and Monitoring, of New Infectious Diseases The first case, which is described in more detail in (Pruyt and Hamarat 2010; Pruyt et al. 2013), relates to assessing outbreaks of new flu variants. Outbreaks of new (vari- ants of) infectious diseases are deeply uncertain. For example, in the first months after the first reports about the outbreak of a new flu variant in Mexico and the USA, much remained unknown about the possible dynamics and consequences of this pos- sible epidemic/pandemic of the new flu variant, referred to today as new influenza A(H1N1)v. Table 5.1 shows that more and better information became available over time, but also that many uncertainties remained. However, even with these remaining uncertainties, it is possible to model and simulate this flu variant under deep uncer- tainty, for example with the simplistic simulation model displayed in Fig. 5.2, since flu outbreaks can be modeled. Simulating this model thousands of times over very wide uncertainty ranges for each of the uncertain variables generates the 3D cloud of potential outbreaks dis- played in Fig. 5.3a. In this figure, the worst flu peak (0–50 months) is displayed on the X-axis, the infected fraction during the worst flu peak (0–50 %) is displayed on the Y -axis, and the cumulative number of fatal cases in the Western world (0– 50.000.000) is displayed on the Z-axis. This 3D plot shows that the most catastrophic outbreaks are likely to happen within the first year or during the first winter season following the outbreak. Using machine learning algorithms to explore this ensemble of simulation runs helps to generate important policy insights (e.g., which policy levers to address). Testing different variants of the same policy shows that adaptive policies outperform their static counterparts (compare Fig. 5.3b and c). Figure 5.3d finally shows that adaptive policies can be further improved using multi-objective robust optimization. However, taking deep uncertainty seriously into account would require simulating more than a single model from a single modeling method: it would be better to simultaneously simulate CAS, ABM, SD, and hybrid models under deep uncertainty and use the resulting ensemble of simulation runs. Moreover, near real-time geo- spatial data (from twitter, medical records, etc.) may also be used in combination with simulation models, for example, to gradually reduce the ensemble of model- generated data. Both suggested improvements would be possible today. 5.5.2 Integrated Risk-Capability Analysis under Deep Uncertainty The second example relates to risk assessment and capability planning for National Safety and Security. Since 2001, many nations have invested in the development of all-hazard integrated risk-capability assessment (IRCA) approaches. All-hazard IRCAs integrate scenario-based risk assessment, capability analysis, and capability- based planning approaches to reduce all sorts of risks—from natural hazards, over technical failures to malicious threats—by enhancing capabilities for dealing with w.jager@rug.nl 5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling 85 T ab le 5. 1 In fo rm at io n an d un kn ow ns pr ov id ed by th e E ur op ea n C en tr e fo r D is ea se P re ve nt io n an d C on tr ol (E C D C ) fr om 24 A pr il un ti l 21 A ug us t D at e 24 A pr il 30 A pr il 08 M ay 20 M ay 12 Ju ne 20 Ju ly 21 A ug us t In fe ct iv it y U nk no w n U nk no w n U nk no w n U nk no w n U nk no w n U nk no w n U nk no w n R o U nk no w n U nk no w n 1– 2; pr ob . 1– 2; pr ob . – – [R ≤ 2] 1. 4– 1. 9 1. 4– 1. 6 Im m un it y U nk no w n U nk no w n In di ca ti on s Id em Id em Id em Id em (e ld er ly ) V ir ul en ce U nk no w n U nk no w n U nk no w n U nk no w n U nk no w n M il d an d Id em se lf -l im it in g In cu ba ti on U nk no w n U nk no w n L on g ta il ? – M ed ia n 3– 4 da ys Id em Id em pe ri od (u p to 8 da ys ) ra ng e 1– 7 da ys C F R M ex ic o 17 % ? – 4% ? 2% ? 0. 4– 1. 8% ? – – C F R U S A U nk no w n U nk no w n 0. 1% ? 0. 1% ? 0. 2% ? 0. 4% ? – C F R a U K U nk no w n U nk no w n U nk no w n U nk no w n U nk no w n 0. 3% (– 1% )? 0. 1– 0. 2% ? A ge di st ri bu ti on U nk no w n U nk no w n E ld er ly le ss af fe ct ed ? – S ke w ed to w .y ou ng er Id em Id em A nt iv ir al su sc ep . U nk no w n P os si bl e In di ca ti on s – – – – % as ym pt om at ic U nk no w n U nk no w n U nk no w n U nk no w n U nk no w n In di ca ti on s 33 –5 0% F ut ur e? U nk no w n U nk no w n U nk no w n U nk no w n U nk no w n U nk no w n U nk no w n a C F R st an ds fo r ca se fa ta li ty ra ti o w.jager@rug.nl 86 E. Pruyt F ig .5 .2 R eg io n 1 of a tw o- re gi on sy st em dy na m ic s (S D ) fl u m od el w.jager@rug.nl 5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling 87 Fig. 5.3 3D scatter plots of 20,000 Latin-Hypercube samples for region 1 with X-axis: worst flu peak (0–50 months); Y -axis: infected fraction during the worst flu peak (0–50 %); Z-axis: fatal cases (0–5 × 107) them. Current IRCAs mainly allow dealing with one or a few specific scenarios for a limited set of relatively simple event-based and relatively certain risks, but not for dealing with a plethora of risks that are highly uncertain and complex, combina- tions of measures and capabilities with uncertain and dynamic effects, and divergent opinions about degrees of (un)desirability of risks and capability investments. The next generation model-based IRCAs may solve many of the shortcomings of the IRCAs that are currently being used. Figure 5.4 displays a next generation IRCA for dealing with all sorts of highly uncertain dynamic risks. This IRCA approach, described in more detail in Pruyt et al. (2012), combines EMA and modeling and simulation, both for the risk assessment and the capability analysis phases. First, risks—like outbreaks of new flu variants—are modeled and simulated many times across their multidimensional uncertainty spaces to generate an ensemble of plausible risk scenarios for each of the risks. Time series classification and machine learning techniques are then used to identify much smaller ensembles of exemplars that are representative for the larger ensembles. These ensembles of exemplars are then used as inputs to a generic capability analysis model. The capability analysis model is subsequently simulated for different capabilities strategies under deep uncertainty (i.e., simulating the uncertainty pertaining to their effectiveness) over all ensembles of exemplars to calculate the potential of capabilities strategies to reduce these risks. w.jager@rug.nl 88 E. Pruyt Fig. 5.4 Model-based integrated risk-capability analysis (IRCA) Finally, multi-objective robust optimization helps to identify capabilities strategies that are robust. Not only does this systems-of-systems approach allow to generate thousands of variants per risk type over many types of risks and to perform capability analy- ses across all sorts of risk and under uncertainty, it also allows one to find sets of capabilities that are effective across many uncertain risks. Hence, this integrated model-based approach allows for dealing with capabilities in an all-hazard way under deep uncertainty. This approach is currently being smartened using adaptive output-oriented sam- pling techniques and new time-series classification methods that together help to identify the largest variety of dynamics with the minimal amount of simulations. Covering the largest variety of dynamics with the minimal amount of exemplars is desirable, for performing automated multi-hazard capability analysis over many risks is—due to the nature of the multi-objective robust optimization techniques used— computationally very expensive. This approach is also being changed from a multi- model approach into a multi-method approach. Whereas, until recently, sets of SD models were used; there are good reasons to extend this approach to other types of systems modeling approaches that may be better suited for particular risks or—using multiple approaches—help to deal with methodological uncertainty. Finally, settings of some of the risks and capabilities, as well as exogenous uncertainties, may also be fed with (near) real-world data. 5.5.3 Policing Under Deep Uncertainty The third example relates to another deeply uncertain issue, high-impact crimes (HIC). An SD model and related tools (see Fig. 5.5) were developed some years ago in view of increasing the effectiveness of the fight against HIC, more specifically the fight against robbery and burglary. HICs require a systemic perspective and approach: w.jager@rug.nl 5 From Building a Model to Adaptive Robust Decision Making Using Systems Modeling 89 Fig. 5.5 (I) Exploratory system dynamics modelling and analysis (ESDMA) model, (II) interface for policy makers, (III) analytical module for analyzing the high-impact crimes (HIC)system under deep uncertainty, (IV) real-world pilots based on analyses, and (V) monitoring of real-world data from the pilots and the HIC system These crimes are characterized by important systemic effects in time and space, such as learning and specialization effects, “waterbed effects” between different HICs and precincts, accumulations (prison time) and delays (in policing and jurisdiction), preventive effects, and other causal effects (ex-post preventive measures). HICs are also characterized by deep uncertainty: Most perpetrators are unknown and even though their archetypal crime-related habits may be known to some extent at some point in time, accurate time and geographically specific predictions cannot be made. At the same time, is part of the HIC system well known and is a lot of real-world information related to these crimes available. Important players in the HIC system besides the police and (potential) perpetrators are potential victims (households and shopkeepers), partners in the judicial system (the public prosecution service, the prison system, etc.). Hence, the HIC system is dynamically complex, deeply uncertain, but also data rich, and contingent upon external conditions. The main goals of this pilot project were to support strategic policy making under deep uncertainty and to test and monitor the effectiveness of policies to fight HIC. The SD model (I) was used as an engine behind the interface for policy makers (II) to explore plausible effects of policies under deep uncertainty and identify real- world pilots that could possibly increase the understanding about the system and effectiveness of interventions (III), to implement these pilots (IV), and monitor their outcomes (V). Real-world data from the pilots and improved understanding about the functioning of the real system allow for improving the model. w.jager@rug.nl 90 E. Pruyt Today, a lot of real-world geo-spatial information related to HICs is available online and in (near) real time which allows to automatically update the data and model, and hence, increase its value for the policy makers. The model used in this project was an ESDMA model. That is, uncertainties were included by means of sets of plausible assumptions and uncertainty ranges. Although this could already be argued to be a multi-model approach, hybrid models or a multi-method approach would really be needed to deal more properly with systems, agents, and spatial characteristics. Moreover, better interfaces and connectors to existing ICT systems and databases would also be needed to turn this pilot into a real decision-support system that would allow chiefs of police to experiment in a virtual world connected to the real world, and to develop and test adaptive robust policies on the spot. 5.6 Conclusions Recent and current evolutions in modeling and simulation together with the recent explosive growth in computational power, data, social media, and other evolutions in computer science have created new opportunities for model-based analysis and decision making. Multi-method and hybrid modeling and simulation approaches are being devel- oped to make existing modeling and simulation approaches appropriate for dealing with agent system characteristics, spatial and network aspects, deep uncertainty, and other important aspects. Data science and machine learning techniques are currently being developed into techniques that can provide useful inputs for simulation models as well as for building models. Machine learning algorithms, formal model analysis methods, analytical approaches, and new visualization techniques are being devel- oped to make sense of models and generate useful policy insights. And methods and tools are being developed to turn intuitive policy making into model-based policy design. Some of these evolutions were discussed and illustrated in this chapter. 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Facilitating team learning using system dynamics. Wiley, Chichester Yücel G (2012) A novel way to measure (dis)similarity between model behaviors based on dynamic pattern features. In: Proceedings of the 30th international conference of the System Dynamics Society, St. Gallen, Switzerland, 22 July–26 July 2012 Yücel G, Barlas Y (2011) Automated parameter specification in dynamic feedback models based on behavior pattern features. Syst Dyn Rev 27(2):195–215 w.jager@rug.nl Chapter 6 Features and Added Value of Simulation Models Using Different Modelling Approaches Supporting Policy-Making: A Comparative Analysis Dragana Majstorovic, Maria A. Wimmer, Roy Lay-Yee, Peter Davis and Petra Ahrweiler Abstract Using computer simulations in examining, explaining and predicting so- cial processes and relationships as well as measuring the possible impact of policies has become an important part of policy-making. This chapter presents a compara- tive analysis of simulation models utilised in the field of policy-making. Different models and modelling theories and approaches are examined and compared to each other with respect to their role in public decision-making processes. The analysis has shown that none of the theories alone is able to address all aspects of complex policy interactions, which indicates the need for the development of hybrid simula- tion models consisting of a combinatory set of models built on different modelling theories. Building such hybrid simulation models will also demand the development of new and more comprehensive simulation modelling platforms. D. Majstorovic (�) · M. A. Wimmer University of Koblenz-Landau, Koblenz, Germany e-mail: majstorovic@uni-koblenz.de M. A. Wimmer e-mail: wimmer@uni-koblenz.de R. Lay-Yee · P. Davis Centre of Methods and Policy Application in the Social Sciences (COMPASS Research Centre), University of Auckland, Private Bag 92019, 1142 Auckland, New Zealand e-mail: r.layyee@auckland.ac.nz P. Davis e-mail: pb.davis@auckland.ac.nz P. Ahrweiler EA European Academy of Technology and Innovation Assessment GmbH, Bad Neuenahr-Ahrweiler, Germany e-mail: Petra.Ahrweiler@ea-aw.de © Springer International Publishing Switzerland 2015 95 M. Janssen et al. (eds.), Policy Practice and Digital Science, Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_6 w.jager@rug.nl 96 D. Majstorovic et al. 6.1 Introduction Using computer simulation as a tool in examining, explaining and predicting social processes and relationships started intensively during 1990s (Gilbert and Troitzsch 2005). Since 2000s, a growing recognition of simulation models playing a role in public decision modelling processes can be noted (van Egmond and Zeiss 2010). One reason for this increased attention is that simulation models enable the examination of complex social processes and interactions between different entities and the potential impact of policies. For example, simulation models can be used to examine the impact of measures such as school closure and vaccination in stopping the spread of influenza as the cases described in Sect. 3.1 and 3.2 demonstrate; or to examine the influence of different policies in the early years of life as the case outlined in Sect. 3.3 evidences. This chapter presents a comparative analysis of different simulation models with respect to their role in public decision-making processes. The focus is on investigating the differences between simulation models and their underlying modelling theories in order to find variables that impact the effectiveness of the usage of simulation models in policy-making. The ultimate goal is to provide an understanding of the peculiarities and the added value of different kinds of simulation models generated on the basis of particular modelling approaches. The chapter also aims at giving indications of how existing approaches to policy simulation can and should be combined to effectively support public policy-making in a comprehensive way. This comparative analysis was performed as part of the eGovPoliNet1 initia- tive, which aims at developing an international multidisciplinary policy community in information and communication (ICT) solutions for governance and policy modelling. eGovPoliNet brings researchers from different disciplines and com- munities together for sharing ideas, discussing knowledge assets and developing joint research findings. The project fosters a multidisciplinary approach to in- vestigate different concepts in policy modelling. In investigating these concepts, researchers from different disciplines (such as information systems, e-government and e-participation, computer science, social sciences, sociology, psychology, or- ganisational sciences, administrative sciences, etc.) collaborate to study the—so far mostly mono-disciplinary—approaches towards policy modelling. With this ap- proach, eGovPoliNet aims at contributing to overcoming the existing fragmentation of research in policy modelling across different disciplines. The research carried out in this paper was based on the literature study of policy modelling approaches whereby the authors collaborated with expertise from their own academic background. On the other hand, a comparative analysis of five differ- ent simulation models was performed using a framework of comparison developed along the eGovPoliNet initiative. The selection of the cases was based on the authors’ 1 eGovPoliNet—Building a global multidisciplinary digital governance and policy modelling re- search.and practice community. See http://www.policy-community.eu/ (last access: 28th July 2014). w.jager@rug.nl 6 Features and Added Value of Simulation Models Using Different Modelling. . . 97 access to and involvement in generating the particular models. Accordingly, the re- spective authors also elaborated the individual descriptions of the simulation models. The subsequent comparison and synthesis of simulation models based on different modelling approaches was done in a collaborative way. Findings were developed jointly and present views of different disciplines concerning the role of simulation models in policy modelling as well as possible ways of advancement of the usage of combinations of simulation models and joint elaborations thereof. The key research questions guiding the comparative investigation of different simulation models are twofold: (1) What particular modelling theories, frameworks and/or methods build the theoretical and methodical foundations of simulation mod- els in policy modelling? (2) In what way do simulation models developed on the basis of different foundations (cf. question (1)) differ and what lessons can be drawn from using different simulation models in policy modelling? To answer research question (1), the different theoretical and methodical grounds of simulation approaches will be studied, while for research question (2), five different simulation models will be compared and analysed. The chapter is organised as follows: Sect. 6.2 first provides an understanding of the key terms and subsequently examines three different and widely used theories and approaches to simulation modelling (system dynamics, micro-simulation and agent- based modelling (ABM)) in order to establish common grounds for the research context. Subsequently, a framework for the comparative analysis of simulation mod- els based on these modelling paradigms is introduced and five different simulation models are analysed in Sect. 6.3 (VirSim, MicroSim, MEL-C, OCOPOMO’s Kosice model and SKIN). Then in Sect. 6.4, these models are compared and discussed to extract features of usage, benefits and the main characteristics of specific approaches to simulation modelling in policy-making. Some reflections on the research and practice implications as well as further research needs are also drawn from the com- parative analysis. We conclude with a reflection on the results and insights gathered in Sect. 6.5. 6.2 Foundations of Simulation Modelling Gilbert and Troitzsch define a simulation model as ‘a simplification—smaller, less detailed, less complex, or all of these together—of some other structure or system’ (Gilbert and Troitzsch 2005). A simulation model is a computer program that captures the behaviour of a real-world system and its input and possible output processes. It relies on data from the real world to create an artificial one that mimics the original but upon which experiments can be performed (Gilbert and Troitzsch 2005. According to Gilbert and Troitzsch, simulation models are useful for many reasons—it is easier, less expensive and in many cases the only appropriate way (e.g. spreading of a disease) or only feasible way (e.g. the consequences of some policy decisions can be seen only many years ahead as is the case with urbanisation) of examining possible impacts of policies. The output of a simulation is a set of measurements describing w.jager@rug.nl 98 D. Majstorovic et al. the observable reactions and a performance of a real-world system. For example, simulation models may produce forecasts or projections as output into the future, hence supporting policy-making processes, while stakeholders could use simulation models as support tools in examining possible impacts of different policies(Gilbert and Troitzsch 2005. Simulation models can also be used for a better understanding of real-world processes and relationships (Gilbert and Troitzsch 2005, for entertainment (e.g. the simulation game MoPoS2, where a player is a central bank governor), as well as for education and training purposes (e.g. the simulation model GAIM3). On a higher abstraction level, simulation models can even be used for the formalisation of social theories producing social science specifications (Gilbert and Troitzsch 2005). Different paradigms (i.e. approaches and theories) to simulation modelling ex- ist in the literature. They vary in aspects of the reality they model as well as in methods they use to produce a simulation model (Gilbert and Troitzsch 2005. Three approaches are considered in this chapter: system dynamics as a representative type of macro-simulation, ABM as a representative type of modelling social behaviour of groups, and micro-simulation focusing on modelling individual’s evolution. These approaches have been selected as they are well-known and widely used. The system dynamics approach models a situation at a global level to describe a real-world system using analytical means via systems of differential equations (Gilbert and Troitzsch 2005. A real-world system is described and analysed as a whole at the macro-level (Forrester 1961) and represented using flow diagrams and internal feedback loops (Harrison et al. 2007). Such a model does not require much data and the output of the model consists of plots describing behaviour and the changes of the initial values of the variables and parameters of the model over time. To describe behaviour of the real-world process accurately, a model needs to be run many times with different parameter values (Maria 1997). A typical use of system dynamics models is macro-economic modelling as well as for describing the impact of policies during, for example, a spread of a disease. According to (Astolfi et al. 2012), system dynamics models are well suited for predicting short-term policy impacts. Complex policy issues require approaches that enable research synthesis and the use of systems thinking (Milne et al. 2014). Micro-simulation modelling has the potential to represent systems and processes in various social domains and to test their functioning for policy purposes (Anderson and Hicks 2011; Zaidi et al. 2009). A micro-simulation model is based on empirical individual-level data and it can account for social complexity, heterogeneity, and change (Orcutt 1957; Spielauer 2011). Micro-simulation operates at the level of individual units, each with a set of associated attributes as a starting point. A set of rules, for example equations derived from statistical analysis of (often multiple) survey data sets, is then applied in a stochastic manner to the starting sample to simulate changes in state or behaviour. 2 MoPoS—A monetary Policy Simulation Game (Lengwiler 2004). 3 GAIM—Gestione Accoglienza IMmigrati (Sedehi 2006) is used for the training of foreign intercultural mediators in the immigration housing management courses. w.jager@rug.nl 6 Features and Added Value of Simulation Models Using Different Modelling. . . 99 Modifications of influential factors can then be carried out to test hypothetical ‘what if’ scenarios on a key outcome of policy interest (Davis et al. 2010). Micro-simulation can integrate, and accommodate the manipulation of, and the effects of variables across multiple model equations (often derived from multiple data sources) in a single simulation run. Thus, each otherwise separate equation is given its social context and influence among the other equations, representing a system of interdependent social processes. Gilbert defines ABM as ‘a computational method that enables a researcher to cre- ate, analyse and experiment with models composed of agents that interact within an environment’ (Gilbert 2007). In artificial intelligence, agents are ‘self-contained pro- grams that can control their own actions based on their perceptions of the operating environment’ (Gilbert and Troitzsch 2005). Applied to social processes, agents are individuals or groups of individuals aware of their environment and at the same time proactive in interactions with each other and their surroundings. Agent-based simu- lation models capture and explain the behaviour of agents and the dynamics of their social interactions, and they usually do not assume future predictions (Srbljanovic and Skunca 2003, Gilbert and Troitzsch 2005). ABM is considered a powerful tool for developing, testing and formalising social theories and examining complex social interactions (Gilbert and Troitzsch 2005). For example, agent-based simulations are able to describe complex social phenomena at a global macro-level emerging from simple micro-level interactions between the agents (Srbljanovic and Skunca 2003). The application of ABM offers two major advantages (Gilbert and Troitzsch 2005): a capability to show from where collective phenomena come based on isolation of crit- ical behaviour and the main agents, and a possibility to explore various alternatives of development. Building a simulation model means developing a computer program either from scratch or from adapting existing models. To achieve this, tools such as AnyLogic4, NetLogo5, etc.6 are being used. Prior to programming the simulation model, ap- propriate knowledge about the policy context that should be explored needs to be collected. Different literature provide indications of the steps in an ordered process as shown in Fig. 6.1. It is to be noted that not necessarily every step is carried out by a simulation modeller. Depending on the expertise of programmers or policy analysts, steps 1 and 2 are in some cases merged, or steps 3 and 4 are not differentiated. In the simplest case, an expert policy modeller might even just perform steps 1, 4 and 5. However, to support a wider understanding of policy modelling, the sharing of the overall concept of analysis and programming, and a higher quality of simulation models, the performance of all five steps is highly recommended. The first three steps to generate a simulation model are to (1) collect source data, (2) to develop a conceptual model and (3) to design the simulation model. These 4 http://www.anylogic.com/ (last access: 28th July 2014). 5 http://ccl.northwestern.edu/netlogo/ (last access: 28th July 2014). 6 A more detailed overview of tools and technologies supporting policy making is provided in (Kamateri et al. 2014). w.jager@rug.nl 100 D. Majstorovic et al. Fig. 6.1 Generic steps for developing simulation models Program the simula on model Conceptual modelling Simula on model design Analysis of source data Verifica on and Valida on steps form the analytical work of policy modelling and can also be labelled ‘policy analysis’ and ‘conceptual modelling’. The ways and methods to collect and analyse source data (step 1) depend on the type of simulation model to be generated and its underlying modelling paradigm. For example, micro-simulation is based on large amounts of representative data gathered on individuals; it considers characteristics of individuals and is able to reproduce social reality (Martini and Trivellato 1997). Micro-simulation is beneficial in predicting both short-term as well as long-term impacts of policies (Gilbert and Troitzsch 2005). System dynamics models the real- world system as a whole, i.e. at the macro-level (Forrester 1961) by using (a small set of) aggregated data. ABM is valuable for describing and explaining complex social interactions and behaviour, thus contributing to the understanding of a real- world social system and to a better management of different social processes (Gilbert and Troitzsch 2005). The focus is on groups and individuals interacting in a social system and the amount of data needed for developing the simulation model can be considered moderate. The particular approach of simulation modelling determines the complexity of data analysis (i.e. highly complex and intense for micro-simulation, moderate to high complexity for ABM (depending on the number of agents and the aggregation concept), and rather low for system dynamics). Inputs for data analysis are features, descriptions, relationships and specifications of the observed real-world system (Gilbert and Troitzsch 2005). Data analysis can be performed through many different ways ranging from qualitative and quantitative data analysis methods of the social sciences (Mayring 2011) to action research (Greenwood and Levin 2006), design research (Collins et al. 2004) and active stakeholder engagement using, e.g. scenario-building and online citizen participation methods (Wimmer et al. 2012). The second step—conceptual modelling—is not always explicitly implemented as already mentioned. It is a step that is widely used in action research and in design w.jager@rug.nl 6 Features and Added Value of Simulation Models Using Different Modelling. . . 101 research. Newer approaches to policy modelling are based on the value of concep- tualising a policy context and accordingly building conceptual models from the data analysed, as is, e.g. described in (Scherer et al. 2013). Since simulation models are simplifications of reality (Zeigler 1976), conceptual modelling, in practical terms, means to decide on which characteristics of the real-world system are to be included in a simulation model and which ones are not (Gilbert and Troitzsch 2005). In the third step—design of the actual simulation model—the programming of the simulation model is prepared by building a construct of the simulation model (again dependent on the modelling paradigm). The fourth step—programming the simulation model— involves putting hands on writing the code using a particular tool for programming. The fifth step—verification and validation—aims to check if a simulation model behaves as desired (i.e. verification) and whether the model describes the intended real-world system in a satisfactory way and gives reliable outputs (i.e. validation). Validation can be conducted by comparing known behaviour and parameters of the real-world system with the outputs of a simulation model (Maria 1997). Based on the insights of different paradigms of simulation models, the next sections analyse and compare different models built on these theories and approaches. 6.3 Analysis of Simulation Models of Different Modelling Approaches In this section, the analysis of five simulation models is presented with respect to their contribution to policy modelling in different public domains. The main goal is to describe and compare different simulation models in order to identify similarities and differences that suggest approaches, tools and techniques that are useful and effective in different policy modelling contexts. For the comparative analysis, eGov- PoliNet developed a framework which serves as a template to ensure comparability across particular aspects of study and to simplify understanding. The framework is divided into three parts: (1) abstract, which gives a brief summary of the model under investigation, and its context; (2) metadata, providing general information such as name of the model, developer, the publication date, background documents used in developing the model, references, tools needed to run the simulation model (for an ordinary user), and a reference to the source of the model; and (3) conceptual aspects of interest in the comparison such as disciplines involved in the model development, underlying theory, particular methods applied to develop the model, technical frame- works and tools used to develop the simulation model7, application domain of the model, constraints of using the model in a particular way, examples of (re)use of the formal model (i.e. giving reference to policy cases and projects where the model 7 A comparative analysis of tools and technical frameworks is provided in (Kamateri et al. 2014). w.jager@rug.nl 102 D. Majstorovic et al. Table 6.1 Simulation models examined in the comparative analysis Based on theory Simulation model System dynamics VirSim—a model to support pandemic policy-making (cf. Sect. 6.3.1) Micro-simulation MicroSim—micro-simulation model: modelling the Swedish population (cf. Sect. 6.3.2) MEL-C—Modelling the early life-course (cf. Sect. 6.3.3) Agent-based modelling (ABM) OCOPOMO’s Kosice case (cf. Sect. 6.3.4) SKIN—simulating knowledge dynamics in innovation networks (cf. Sect. 6.3.5) is/was used), transferability of the simulation model in other domains or disciplinary contexts, and concluding recommendations on the model development and use).8 The simulation models studied in this chapter are based on the modelling paradigms presented in Sect. 6.2, namely system dynamics, micro-simulation, and ABM. Table 6.1 indicates the five simulation models examined in the comparative analysis and presented in the subsections below. It was not the aim of the authors to present an exhaustive list of models but rather a collection that is an informative choice of specific simulation models corresponding to different modelling theories. The models were chosen because the authors had the access to, and knowledge about these simulation models, and they were directly involved in the development of the simulation models analysed in Sect. 3.3, 3.4 and 3.5. The two models described in Sect. 3.1 and 3.2 are interesting for the comparison since they represent the same policy domain and use the same data but represent implementations of different modelling paradigms and methods. The five simulation models are presented in the following subsections. The de- scriptions follow the structure suggested by the framework proposed by eGovPoliNet, i.e. abstract, metadata and conceptual aspects of interest. While the abstract is provided as a narrative paragraph, the metadata and conceptual aspects are each elab- orated in a tabular form. The subsequent Sect. 6.4 provides a comparative discussion of the different models and their added value to policy modelling. 6.3.1 VirSim—A Model to Support Pandemic Policy-Making VirSim simulates the spread of pandemic influenza and enables evaluating the effect of different policy measures (Fasth et al. 2010). The main goal is to find the most optimal policies connected to the starting time and the duration of school closure as 8 The framework is published in Annex I to technical report D 4.2 of eGovPoliNet: Maria A. Wimmer and Dragana Majstorovic (Eds.): Synthesis Report of Knowledge Assets, including Visions (D 4.2). eGovPoliNet consortium, 2014, report available under http://www.policy- community.eu/results/public-deliverables/ (last access: 28th July 2014). w.jager@rug.nl 6 Features and Added Value of Simulation Models Using Different Modelling. . . 103 well as the pace and the vaccination coverage. Using the model, it is also possible to estimate public costs due to the absence of staff from work during sick leave. The model considers real population data in Sweden at both national and regional levels (Fasth et al. 2010). In VirSim, the population is divided into three age groups: individuals less than 20 years old, those between 20 and 59 years, and people aged 60 years and more. It is initially assumed that influenza spreads within and between groups with different probabilities. For each age group, a SEIR model (Susceptible, Exposed, Infected, and Recovered) is constructed, which represents the dynamics of spread of the disease. This means that a healthy person starts as a susceptible (S), becomes exposed (E), then infected (I) and, after some time recovered (R) (or dead). VirSim supports scenario analysis (i.e. ‘what-if’ analysis), which means that a user can combine a number of different parameters producing ‘real’ scenarios and examine the impact of policies while asking ‘what could happen if we apply policy XY’ (Fasth et al. 2010) (Table 6.2). Table 6.2 Analysis of metadata and conceptual data of the simulation model VirSim Metadata Name VirSim Developer Tobias Fasth, Marcus Ihlar, Lisa Brouwers Publication date 2010 Background documents To segregate the Swedish population into three age groups: (Statistics Sweden (Statistiska centralbyrån, SCB 2009) To estimate frequency of social contacts between and within age groups: (Wallinga 2006) To decide on the duration of a latent period: (Carrat et al. 2008, Fraser et al. 2009) Reference(s) (Fasth et al. 2010) Tools needed to run the model Web browser, Internet Source of the model http://www.anylogic.com/articles/virsim-a-model-to- support-pandemic-policy-making (last access: 28th July 2014) http://people.dsv.su.se/∼maih4743/VirSim/VirSim.html (needs Java Platform SE 7 U activated; last access: 28th July 2014) Conceptual aspects Discipline(s) Health science, Information technology/E-Government Based on theory System dynamics Developed through method SEIR model (susceptible, exposed, infected, recovered) Technical framework/tools used for development AnyLogic Application domain(s) Policy-making under pandemic influenza w.jager@rug.nl 104 D. Majstorovic et al. Table 6.2 (continued) Metadata Constraints of using the model in a particular way The VirSim model does not take into account parameters that are also important for the transmission and spreading of the influenza virus, such as effect of weather and temperature conditions, geographical differences between regions as well as diverse social structures including travelling frequency, gender and hygiene habits. It is not possible to analyse many of the missing parameters since the underlying SEIR model and system dynamics method do not take into account social differences. Hence, the same infection probability was assigned to all people within the three age groups Examples of use (projects/cases) Policy-making under pandemic influenza in Sweden in 2009. Tested policies were vaccination and school closure (Fasth et al. 2010) Transferability of formal model in other domains or disciplinary contexts The initial values for all parameters are provided, for example, starting time of vaccination and the infection risk for different age groups, based on the documents and the data available in the time of the development of the model. However, VirSim allows for the change of all parameter values, including those initially assumed. This assures that the model is re-usable when other data become available. To our knowledge, the model is not transferable to other domains and contexts since it does not allow for a change of the parameters as such, their number, and the underlying differential equations Concluding remarks on simulation model development and/or use VirSim runs fast and a user can easily manipulate different parameters. However, the user interface does not include descriptions of the parameters; a user has to guess their meaning and a range of values, based on their names. In some cases, this is difficult, for example, for the parameter ‘vaccination . . . starts after’ with the initial value of 147—it is not clear for what the given initial value stands. Apart from this issue, the model is intuitive and easy to work with. The model is based on the scenario analysis—a user posts a question (‘what could happen if we apply certain policy under certain conditions . . . ’) and gets the answer in a form of suitable plots. This allows policy-making officials to discuss policies further towards finding the most suitable ones. To provide accurate and significant results, VirSim uses real population data in Sweden, at the national and regional level (Fasth et al. 2010). To use the simulation model in similar contexts, the model development should become flexible to support the definition of custom variables and at least some classes of differential equations that are suitable for modelling similar phenomena. Also, based on supported types of processes, the description of possible domains of application to which the model could be transferred would be helpful w.jager@rug.nl 6 Features and Added Value of Simulation Models Using Different Modelling. . . 105 6.3.2 MicroSim—Micro-simulation Model: Modelling the Swedish Population According to Brouwers et al, MicroSim addresses the problem of the spread of influenza in Sweden. It is an event-driven micro-simulation model with discrete time steps of an hour, developed for exploring the impact of different intervention policies based on vaccination, isolation and social distancing. Each person living in Sweden was modelled in many details, including age, family status, employment details, and important geographical data, such as home and workplace coordinates. Such a modelling strategy provided a fine-grained differentiation between age groups, people’s daily routines and their educational level. This enabled examining the spread of influenza through different social contacts as well as analysing the spatial spread of the disease within the time range of 1 h (Brouwers et al. 2009a, 2009b) (Table 6.3) Table 6.3 Analysis of metadata and conceptual data of the simulation model MicroSim Metadata Name MicroSim—micro-simulation model: modelling the Swedish population Developer Lisa Brouwers, Martin Camitz, Baki Cakici, Kalle Mäkilä, Paul Saretok Publication date 2009 Background documents MicroSim uses registry data obtained from Statistics Sweden (Statistiska centralbyrån, SCB)a to generate the simulated population, in particular: National Population Register (2002) to describe age, marital status, children, employment, IDs father and mother; Employment Register (2002) to describe company, workplace, branch, municipality of the workplace for each individual; Employment Register (2002) to obtain family household coordinates, workplace coordinates, and school coordinates Reference(s) (Brouwers et al. 2009a, 2009b) Tools needed to run the model Executable that runs within C++ environment Source of the model Available on demand from http://arxiv.org/abs/0902.0901 (last access: 28th July 2014) Conceptual aspects Discipline(s) Health science, Information technology/E-Government Based on theory Micro-simulation Developed through method Population analysis Framework/tools used for development C++ Application domain(s) Policy-making under pandemic influenza w.jager@rug.nl 106 D. Majstorovic et al. Table 6.3 (continued) Metadata Constraints of using the model in a particular way The model relies on particular data from Sweden and therefore cannot be used for other countries. Also, due to possible migrations and changes in structure of Swedish population, the model has to be validated against new available data Examples of use (projects/cases) Policy-making under pandemic influenza in Sweden in Autumn of 2009 Transferability of formal model in other domains or disciplinary contexts The model can be used as a basis for examining effects of different policies as well as dependencies in the real-world systems and processes based on social and geographical distributions Concluding remarks on simulation model development and/or use While micro-simulation models in general use only sample data of the population, MicroSim uses personal, employment and geographic data of the complete Swedish population (approximately 9 million people), which provides an explicit enhancement of the model’s accuracy and reliability. Such detailed representation provides conditions suitable for realistic simulations of influenza outbreaks in Sweden. However, micro-simulation models based on the ontology of the population is not robust towards demographic changes in the social structure of a population a http://www.scb.se (last access: 28th July 2014). 6.3.3 MEL-C—Modelling the Early Life-Course MEL-C is a Knowledge-based Inquiry tool With Intervention modelling (KIWI) developed on the early life-course as a decision support aid to policy analysts and advisors in New Zealand. Underlying the tool is a dynamic discrete-time micro- simulation model using a social determinants framework to predict child outcomes, for which the key parameters have been estimated from existing longitudinal cohort studies in New Zealand, initially the Christchurch Health and Development Study. These parameters were applied to a starting sample synthesised from a combination of data from the national census and from the longitudinal studies. Thus a set of synthetic representative early life histories was created that reproduced patterns found in the original data. The tool can be interrogated with realistic policy scenarios by changing baseline features or parameters in the model and observing the effect on outcomes, for example, ‘what if’ the initial social determinants were different and what would be their impact. The model content, tool interface and inquiry system have been developed in cooperation with central government policy advisors drawn from the agencies with a special interest in the early life-course (Mannion et al. 2012) (Table 6.4). w.jager@rug.nl 6 Features and Added Value of Simulation Models Using Different Modelling. . . 107 Table 6.4 Analysis of metadata and conceptual data of the simulation model MEL-C Metadata Name MEL-C—modelling the early life-course Developer COMPASSa Publication date 2014 Background documents (Fergusson et al. 1989; Solar and Irwin 2010) Reference(s) (Mannion et al. 2012; Milne, et al. 2014; McLay et al. 2014; Lay-Yee et al. 2014) Tools needed to run the model The MEL-C executable, which includes JAMSIM (consisting of ASCAPE, JAVA and R) and simulation code run with R and tailored functions from the R Simario package developed by COMPASS Source of the model See http://code.google.com/p/jamsim/ for JASMIM (last access: 28th July 2014) See http://code.google.com/p/simario/ for R SIMARIO package (last access: 28th July 2014) MEL-C simulation model accessible on request from the COMPASS research centre Conceptual aspects Discipline(s) Social and health sciences (sociology, psychology, epidemiology), statistics, computer science, policy sciences Based on theory Child development, Social determinants of health, Micro-simulation Developed through method Regression analysis R and JAVA programming Micro-simulation modelling End-user engagement Cluster matching and data imputation Framework/tools used for development MEL-C as a single executable software application in which users can interrogate the model from the ‘front end’ and not need to deal with the ‘behind-the-scenes’ computer programs and statistical models. The tools used are: Eclipse, StatEt, Git control, Ivy. ASCAPEb and Jamsimc (JAVA) for front end Simario (R)d for execution of models Application domain(s) Early life-course, Health, Justice, Education, Social Policy, Policy scenarios, User interface Constraints of using the model in a particular way Limited by variables available in the source data sets. Relationships between variables are un-directional with no feedback. Scenarios tested involve changing the distribution of variables not the effects (e.g. the effect of X on Y). Potential geographical and period limits of data sources. Discrete time only Examples of use (projects/cases) Illustrative application to social determinants of health and end-user engagement w.jager@rug.nl 108 D. Majstorovic et al. Table 6.4 (continued) Metadata Transferability of formal model in other domains or disciplinary contexts The model is of generic applicability in early life-course analysis. Subject to data availability and funding, it is possible to extend the model to later periods in the life-course and other domains. There may be other dynamic socio-demographic processes where this approach can be applied Concluding remarks on simulation model development and/or use The model is restricted to a notional ‘evidence-based’/ science-informed approach to policy development. The model is conceptually predicated on the primacy of social determinants. The role of stakeholders is limited to the rather formal role of a policy advisor or analyst seeking to weigh different options within a prescribed range. The model is able to reproduce actualities and to produce plausible substantive results in scenario testing. The model has the great potential of combining a realistic data framework with estimates derived from meta-analyses, systematic reviews and other research sources. The model is a simplification of reality but is nevertheless a powerful source of information that can be interrogated by end-users and can be considered alongside other evidence for policy a http://www.arts.auckland.ac.nz/en/about/ourresearch-1/research-centres-and-archives/centre-of- methods-and-policy-application-in-the-social-sciences-compass/about-compass.html (last access: 28th July 2014). b http://ascape.sourceforge.net/ (last access: 28th July 2014). c http://code.google.com/p/jamsim/ (last access: 28th July 2014). d http://code.google.com/p/simario/ (last access: 28th July 2014). 6.3.4 Ocopomo’s Kosice Case Energy policy is increasingly receiving attention, especially in exploring renewable energy sources, energy saving and to raise awareness about energy policy among citizens. The aim of the simulation model developed for the Kosice self-governing region was to capture the behaviour of key stakeholders and decision-makers towards a new energy policy moving to better house insulation and to using renewable en- ergy sources. The renewable energy policy case combined the scenario-method with ABM to explore social behaviour and interrelations between stakeholders, economic conditions of the region and realistic social dynamics. Based on the stakeholder in- puts gathered through scenarios developed via an online e-participation platform, a conceptual model was developed which informed the agent-based simulation model. The simulation model helped to test the effectiveness of various policy options (e.g. to support better insulation of houses, to invest in renewable energy sources such as gas, coal, and biomass, etc.) (Wimmer et al. 2012). A particularity of the model is the possibility to trace evidence data provided in stakeholders’ scenarios or background documents via a conceptual model to inform the simulation model. Accordingly it is possible for stakeholders to navigate from the simulation outputs back to the evidence input (Lotzmann and Wimmer 2013) (Table 6.5). w.jager@rug.nl 6 Features and Added Value of Simulation Models Using Different Modelling. . . 109 Table 6.5 Analysis of metadata and conceptual data of the simulation model developed in OCOPOMO’s Kosice case Metadata Name OCOPOMO’s Kosice case Developer Partners of the OCOPOMO consortium, involving University of Koblenz-Landau, Scott Moss Associates, Technical University of Kosice, Intersoft and Kosice Self-Governing Region (KSR) Publication date 2013 Background documents Based on the OCOPOMO approach (Wimmer et al. 2012) a number of background documents was used, such as: Analysis of structural funds (2007–2013) and Projects Approved in 2009 in KSR Energy policy of KSR (2007) Strategy of Renewable Energy Sources Utilization in KSR (2006) Demographic composition of the households (1996) Annual report 2009, Regulatory Office for Network Industries Regional Statistics Database (2010) Interviews with experts from KSR and local energy providers Reference(s) (Wimmer et al. 2012; Wimmer 2011; Lotzmann and Meyer 2011; Butka et al. 2011; Lotzmann and Wimmer 2013) Tools needed to run the model Collaborative e-participation platform for scenario generation and stakeholder engagement using the ALFRESCOa Web content management system (wiki for scenario generation, discussion, polling) DRAMS (Lotzmann and Meyer 2011)—the Declarative Rule-based Agent Modelling system, Consistent Conceptual Modelling Tool (CCD) (Scherer et al. 2013) Source of the model http://www.ocopomo.eu/results/software-and-models/software- and-model-artefacts/eclipse-based-tools-and-simulation-models (last access: 28th July 2014) Conceptual aspects Discipline(s) Social Science, Information Systems/E-Government Based on theory Agent-based modelling, Model-driven Architecture, Design Research, Stakeholder theory Developed through method Stakeholder engagement through online deliberation, qualitative analysis methods such as workshops and interviews, conceptual modelling using Consistent Conceptual Modelling (CCD), ontology development Framework/tools used for development Eclipse Modelling Framework (EMF)b , Eclipse Graphical Modelling Framework (GMF)c , Graphical Editing Framework (GEF)d , Collaborative participation platform for scenario generation and stakeholder interaction ALFRESCO (wiki, discussion, voting), DRAMS—Declarative Rule-based Agent Modelling System, Consistent Conceptual Modelling (CCD) Tool Application domain(s) The simulation model is used for policy development in the field of energy with the focus on: • Energy efficiency • Decrease of energy consumption (heating) and improved insulation as well as wise spending of energy • Utilisation of renewable energy sources Constraints of using the model in a particular way Agent-based modelling is particularly applicable for examining social behaviour but cannot be the only source for policy-making. w.jager@rug.nl 110 D. Majstorovic et al. Table 6.5 (continued) Metadata Examples of use (projects/cases) Renewable energy and heating in Kosice Self-Governing Region (KSG), Slovakia Housing policy in London, UK Knowledge transfer in Campania Region, Italy Parts of the OCOPOMO simulation environment are also used in the GLODERS projecte Transferability of formal model in other domains or disciplinary contexts Natural conditions of the Kosice region, such as terrain, location of and distance from the renewable energy sources, concentration of housing, available infrastructure, influence the output of the model. Therefore, transferability is restricted, and the use of the model demands for updating the local and natural conditions of a region Concluding remarks on simulation model development and/or use The simulation model is evidence-based and built around the descriptions, expectations, interactions and beliefs of stakeholders in the policy-making process. The modelling process involved stakeholders who expressed their views and concerns on a policy via collaborative scenarios and e-participation tools. They acted as partners and researchers in the modelling process. A key feature of the OCOPOMO policy modelling approach is to engage stakeholders and to ensure traceability from evidence-based input of stakeholders in narrative text to simulation outputs generated through agent-based simulation. A lesson from using a declarative rule programming paradigm as implemented in DRAMS vs. the imperative paradigm in most ABM tools is that the declarative way is more difficult to program and less intuitive for programmers a http://www.alfresco.com/?pi_ad_id=39517088287 (last access: 28th July 2014). b https://www.eclipse.org/modeling/emf/ (last access: 28th July 2014). c http://www.eclipse.org/modeling/gmp/ (last access: 28th July 2014). d http://www.eclipse.org/gef/ (last access: 28th July 2014). e http://www.gloders.eu/ (last access: 28th July 2014). 6.3.5 SKIN—Simulating Knowledge Dynamics in Innovation Networks Simulating Knowledge Dynamics in Innovation Networks (SKIN) is an agent-based model used to understand innovation policy initiatives, which contain heterogeneous agents, who act and interact in a large-scale complex and changing social environ- ment. The agents represent innovative actors who try to sell their innovations to other agents and end users but who also have to buy raw materials or more sophisticated inputs from other agents (or material suppliers) to produce their outputs. This basic model of a market is extended with a representation of the knowledge dynamics in and between the agents. Each agent tries to improve its innovation performance and its sales by improving its knowledge base through adaptation to user needs, incremental or radical learning, and co-operation and networking with other agents (Ahrweiler et al. 2004) (Table 6.6) w.jager@rug.nl 6 Features and Added Value of Simulation Models Using Different Modelling. . . 111 Table 6.6 Analysis of metadata and conceptual data of the simulation model SKIN Metadata Name Simulating knowledge dynamics in innovation networks (SKIN) Developer Gilbert, Nigel; Ahrweiler, Petra; Pyka, Andreas Publication date 2001, with continuous updates since Background documents Literature from Evolutionary Economics, Economic Sociology, and Science and Technology Studies (see body of literature cited in the references as listed next) Reference(s) (Gilbert et al. 2001; Ahrweiler et al. 2004; Gilbert et al. 2007; Pyka et al. 2007; Scholz et al. 2010; Ahrweiler et al. 2011a, 2011b; Gilbert et al. 2014) Tools needed to run the model NetLogo (versions available in alternative languages such as Java) http://ccl.northwestern.edu/netlogo/ (last access: 28th July 2014) Source of the model http://cress.soc.surrey.ac.uk/SKIN/ (last access: 28th July 2014) Conceptual aspects Discipline(s) Economics, sociology, science and technology studies, policy research, business studies Based on theory Evolutionary Economics, Organisational Theory, Organisational Learning, Field Theory, Complex Systems Theory, Agent-based modelling Developed through method Theory formation, empirical research, implementing theoretical concepts and empirical insights, consistent conceptual modelling, agent-based modelling Framework/tools used for development NetLogo Application domain(s) Knowledge-intensive industries, EU framework programmes, national innovation policies, role of specific actors in innovation networks Constraints of using the model in a particular way SKIN is about knowledge and agent networks embedded in a dynamic environment. Not applicable if domain has nothing to do with it. Examples of use (projects/cases) EU projects: Simulating self-organizing innovation networks (SEIN)a , 1998–2001 Network models, governance, and R&D collaboration networks (NEMO)b , 2006–2009 Managing emerging technologies for economic impact (ManETEI)c , 2010–2014 Using network analysis to monitor and track effects resulting from Changes in policy intervention and instruments, (SMART 2010/0025) 2010–2011 Governance of responsible research and innovation (GREAT)d , 2013–2016 w.jager@rug.nl 112 D. Majstorovic et al. Table 6.6 (continued) Metadata Transferability of formal model in other domains or disciplinary contexts SKIN is a multi-disciplinary initiative (see above Discipline(s)) and is therefore used in various disciplinary contexts. However, as it is about knowledge and agent networks embedded in a dynamic environment, it is not applicable if the policy domain is not working with knowledge, innovation and agent networks Concluding remarks on simulation model development and/or use The advantages of using SKIN for policy modelling include: The experiments can be run many times to find statistically average behaviour. Experiments can be used to give an indication of the likely effects of a wide variety of policy measures Empirical ‘Un-observables’ such as the amount of knowledge generated, and the number of proposals started but abandoned before submission, can be measured by instrumenting the simulation The problems include determining: What are the ultimate policy objectives for the support of Research and Development? When were the policies being formulated and by whom? How can the research be presented so that it is interesting and comprehensible to a policy-making audience? a http://ec.europa.eu/research/social-sciences/projects/097_en.html. b http://cress.soc.surrey.ac.uk/SKIN/research/projects/nemo. c http://lubswww.leeds.ac.uk/manetei/home/. d http://www.great-project.eu/ (last access 28th July 2014). 6.4 Comparison of Simulation Models and Discussion of Added Value and Limitations of Particular Simulation Models In Table 6.7, the key elements of the five simulation models introduced in Sect. 3 are compared using the comparison framework above, and summed up on the following aspects: publication date of the model, key aspects of the model, tools needed to run the model, discipline(s), simulation paradigm on which the model is based, method through which the model is developed, framework and/or tools used for the devel- opment of the model, application domain, constraints of using the model, examples of use, model’s transferability to other domains, and limitations and suggestions. As elaborated in Sect. 3.1, the VirSim simulation model examines the effect of different policies to the problem of influenza spread by using the SEIR model for modelling the population on a global (macro-) level. Over time, a person changes between the categories and this flow is described with a set of differential equations (Fasth et al. 2010). The model applies a system dynamics paradigm. The other exam- ple of the same policy domain, the micro-simulation model MicroSim as introduced in Sect. 3.2, applies a different modelling approach to the same problem, where each person is modelled in many details. The spread of influenza is therefore determined by many ‘micro’-level factors. w.jager@rug.nl 6 Features and Added Value of Simulation Models Using Different Modelling. . . 113 T ab le 6. 7 C om pa ri so n of si m ul at io n m od el s V ir S im M ic ro S im M E L -C O C O P M O ’s K os ic e ca se S K IN P ub li ca ti on da te 20 10 20 09 20 14 20 13 20 01 ,w it h fr eq ue nt up da te s si nc e K ey as pe ct s S im ul at es sp re ad of pa nd em ic in fl ue nz a. U se s re al po pu la ti on da ta in S w ed en on na ti on al an d re gi on al le ve l R un s fa st an d al lo w s ea sy m an ip ul at io n of va lu es of pr ed efi ne d pa ra m et er s B as ed on sc en ar io an al ys is . T es te d po li ci es : va cc in at io n an d sc ho ol cl os ur e S im ul at es sp re ad of pa nd em ic in fl ue nz a in S w ed en M od el s co m pl et e S w ed is h po pu la ti on in m an y de ta il s in cl ud in g pe rs on al da ta ,w or ki ng da ta an d ho us eh ol d de ta il s K no w le dg e- ba se d in qu ir y to ol w it h in te rv en ti on m od el li ng D ec is io n su pp or t fo r ea rl y li fe -c ou rs e R ea li st ic po li cy sc en ar io s. D ev el op ed in co op er at io n w it h ce nt ra l go ve rn m en t po li cy ad vi so rs an d th e ag en ci es re le va nt to th e ea rl y li fe -c ou rs e T es ti ng th e ef fe ct iv en es s of pu bl ic po li ci es co nn ec te d to re ne w ab le en er gy so ur ce s an d en er gy sa vi ng un de r di ff er en t co nd it io ns .C ap tu ri ng th e be ha vi ou r of ke y st ak eh ol de rs in th e de ci si on -m ak in g pr oc es s S im ul at io n m od el ev id en ce -b as ed an d bu il t ar ou nd th e de sc ri pt io ns ,e xp ec ta ti on s, in te ra ct io ns an d be li ef s of st ak eh ol de rs .S ta ke ho ld er s ac ti ng as pa rt ne rs in th e m od el li ng pr oc es s R ai si ng aw ar en es s ab ou t en er gy po li cy am on g ci ti ze ns an d st ak eh ol de rs th ro ug h sc en ar io -b as ed m et ho d w it h on li ne e- pa rt ic ip at io n pl at fo rm T ra ce ab il it y of ev id en ce in pu ts to in fo rm th e ag en t- ba se d si m ul at io n m od el F or un de rs ta nd in g in no va ti on po li cy in it ia ti ve s T he ex pe ri m en ts ca n be ru n m an y ti m es to fi nd st at is ti ca ll y av er ag e be ha vi ou r an d th e li ke ly ef fe ct of a w id e va ri et y of po li ci es E m pi ri ca l ‘U n- ob se rv ab le s’ ca n be m ea su re d by in st ru m en ti ng th e m od el w.jager@rug.nl 114 D. Majstorovic et al. T ab le 6. 7 (c on ti nu ed ) V ir S im M ic ro S im M E L -C O C O P M O ’s K os ic e ca se S K IN T oo ls ne ed ed to ru n th e m od el W eb br ow se r, In te rn et E xe cu ta bl e th at ru ns w it hi n C ++ en vi ro nm en t M E L -C ex ec ut ab le , w hi ch in cl ud es JA M S IM (c on si st in g of A S C A P E , JA V A an d R ) an d si m ul at io n co de ru n w it h R an d ta il or ed fu nc ti on s fr om th e R S im ar io pa ck ag e A L F R E S C O w eb co nt en t m an ag em en t fo r st ak eh ol de r en ga ge m en t C C D T oo l an d D R A M S fo r co nc ep tu al m od el li ng an d pr og ra m m in g th e m od el N et L og o. V er si on s al so in ot he r la ng ua ge s, su ch as Ja va D is ci pl in e( s) H ea lt h sc ie nc e, in fo rm at io n te ch no lo gy / e- go ve rn m en t H ea lt h sc ie nc e, in fo rm at io n te ch no lo gy / e- go ve rn m en t S oc ia l an d he al th sc ie nc es (s oc io lo gy , ps yc ho lo gy , ep id em io lo gy ), st at is ti cs , co m pu te r sc ie nc e S oc ia l sc ie nc e, in fo rm at io n sy st em s/ e- go ve rn m en t E co no m ic s, so ci ol og y, sc ie nc e an d te ch no lo gy st ud ie s, po li cy re se ar ch ,b us in es s st ud ie s S im ul at io n pa ra di gm S ys te m dy na m ic s M ic ro -s im ul at io n M ic ro -s im ul at io n A ge nt -b as ed m od el li ng A ge nt -b as ed m od el li ng D ev el op ed th ro ug h m et ho d( s) S E IR m od el P op ul at io n an al ys is , M ic ro -s im ul at io n m od el li ng R eg re ss io n an al ys is ,R an d JA V A pr og ra m m in g, M ic ro -s im ul at io n m od el li ng ,E nd -u se r en ga ge m en t, C lu st er m at ch in g an d da ta im pu ta ti on E vi de nc e- ba se d O C O P O M O po li cy m od el li ng pr oc es s, in vo lv in g st ak eh ol de r en ga ge m en t, sc en ar io -b ui ld in g, co nc ep tu al m od el li ng , ag en t- ba se d m od el li ng us in g de cl ar at iv e ru le en gi ne T he or y fo rm at io n, em pi ri ca l re se ar ch , im pl em en ti ng th eo re ti ca l co nc ep ts an d em pi ri ca l in si gh ts , co ns is te nt co nc ep tu al m od el li ng ,a ge nt -b as ed m od el li ng F ra m ew or k/ to ol s us ed fo r de ve lo pm en t A ny L og ic C ++ pr og ra m m in g fr am ew or k E cl ip se ,S ta tE t, G it co nt ro l, Iv y. A S C A P E an d Ja m si m (J A V A ) fo r fr on t en d S im ar io (R ) fo r ex ec ut io n of m od el s E cl ip se w it h E M F, G M F, G E F, D R A M S ,C C D T oo l an d C C D 2D R A M S tr an sf or m at io n to ol A L F R E S C O W C M S N et L og o w.jager@rug.nl 6 Features and Added Value of Simulation Models Using Different Modelling. . . 115 T ab le 6. 7 (c on ti nu ed ) V ir S im M ic ro S im M E L -C O C O P M O ’s K os ic e ca se S K IN A pp li ca ti on do m ai n H ea lt h an d so ci al po li cy w it h fo cu s on pa nd em ic in fl ue nz a H ea lt h an d so ci al po li cy w it h fo cu s on pa nd em ic in fl ue nz a H ea lt h, Ju st ic e, E du ca ti on ,S oc ia l P ol ic y w it h fo cu s on ea rl y li fe -c ou rs e E ne rg y po li cy w it h fo cu s on us er be ha vi ou r to w ar ds en er gy ef fi ci en cy an d re ne w ab le s K no w le dg e- in te ns iv e in du st ri es ,E ur op ea n an d N at io na l in no va ti on po li ci es C on st ra in ts of th e us ag e M is si ng im po rt an t pa ra m et er s T o al l pe op le w it hi n ag e gr ou ps as si gn ed sa m e pr ob ab il it y U se s pa rt ic ul ar da ta fr om S w ed en N ot ro bu st to ch an ge s in th e po pu la ti on da ta L im it ed by av ai la bl e da ta so ur ce s U n- di re ct io na l re la ti on sh ip s be tw ee n va ri ab le s w it h no fe ed ba ck C ha ng in g th e di st ri bu ti on of va ri ab le s no t th e ef fe ct s. D is cr et e ti m e on ly A ge nt -b as ed m od el li ng ap pl ic ab le fo r ex am in in g so ci al be ha vi ou r bu t ca nn ot be th e on ly so ur ce fo r po li cy -m ak in g M od el ca n be co m e qu it e co m pl ex de pe nd in g on th e nu m be r of ag en ts M od el ap pl ic ab le fo r a do m ai n of kn ow le dg e an d ag en t ne tw or ks em be dd ed in a dy na m ic en vi ro nm en t E xa m pl es of us e P ol ic y- m ak in g un de r pa nd em ic in fl ue nz a in S w ed en in 20 09 P ol ic y- m ak in g un de r pa nd em ic in fl ue nz a in S w ed en in A ut um n of 20 09 Il lu st ra ti ve ap pl ic at io n to so ci al de te rm in an ts of he al th an d en d- us er en ga ge m en t E ne rg y po li cy to ex pl or e st ak eh ol de r be ha vi ou r in K S R ,S K F ur th er m od el s ge ne ra te d: H ou si ng P ol ic y in L on do n, U K K no w le dg e T ra ns fe r su pp or t in C am pa ni a R eg io n, It al y E U pr oj ec ts S E IN , N E M O ,M an E T E I, S M A R T ,G R E A T T ra ns fe ra bi li ty an d re -u sa bi li ty R e- us ab le fo r ot he r av ai la bl e da ta bu t no t fo r ot he r do m ai ns an d co nt ex ts R e- us ab le fo r ex am in in g po li ci es ba se d on de m og ra ph ic s G en er ic ap pl ic ab il it y in ea rl y li fe -c ou rs e an al ys is P os si bl e to ex te nd m od el to la te r pe ri od s in th e li fe -c ou rs e an d to ot he r do m ai ns O th er dy na m ic so ci o- de m og ra ph ic pr oc es se s R e- us e li m it ed to pa rt ic ul ar po li cy an d re gi on al co nt ex t. M od el ca n se rv e as a bl ue pr in t fo r ne w m od el s S K IN ca n be us ed in va ri ou s di sc ip li na ry co nt ex ts w or ki ng w it h kn ow le dg e an d ag en t ne tw or ks em be dd ed in dy na m ic en vi ro nm en ts w.jager@rug.nl 116 D. Majstorovic et al. T ab le 6. 7 (c on ti nu ed ) V ir S im M ic ro S im M E L -C O C O P M O ’s K os ic e ca se S K IN L im it at io ns of m od el s/ su gg es ti on s fo r ex te ns io ns L im it a ti o n s: m od el pa ra m et er s an d eq ua ti on s ar e fi xe d an d ca nn ot be m od ifi ed S u gg es ti o n s: E xt en d th e m od el to su pp or t de fi ni ti on of cu st om va ri ab le s A ll ow fo r m od el li ng si m il ar ph en om en a A ll ow fo r tr an sf er ab il it y to ot he r do m ai ns L im it a ti o n s: th e m od el is no t ro bu st to w ar ds de m og ra ph ic ch an ge s. S u gg es ti o n : al lo w fo r re al is ti c si m ul at io n of in fl ue nz a ou tb re ak s L im it a ti o n s: re st ri ct ed to a no ti on al ‘e vi de nc e- ba se d’ /s ci en ce -i nf or m ed ap pr oa ch co nc ep tu al ly pr ed ic at ed on th e pr im ac y of so ci al de te rm in an ts L im it ed ro le of st ak eh ol de rs . S u gg es ti o n s: C om bi na ti on of a re al is ti c da ta fr am ew or k an d es ti m at es de ri ve d fr om tr ia ls an d sy st em at ic re vi ew s U se fu l fo r en d- us er s L im it a ti o n s: D ec la ra ti ve ru le en gi ne no t ea sy to pr og ra m C on ce pt ua l an d si m ul at io n m od el no t ea sy to un de rs ta nd fo r en d- us er s. S u gg es ti o n s: E xt en d th e C C D to ol an d tr an sf or m at io n to ol to co nn ec t to ot he r A B M to ol s su ch as A ny L og ic ,N et L og o et c. E xt en d th e m od el tr an sf or m at io n to su pp or t al so ba ck w ar d co ns is te nc y fr om si m ul at io n m od el to co nc ep tu al m od el as pr og ra m m er s m ay ch an ge si m ul at io n co de ea si ly L im it a ti o n s: T he ul ti m at e po li cy ob je ct iv es fo r th e su pp or t of re se ar ch an d de ve lo pm en t ne ed ye t to be de te rm in ed S u gg es ti o n s: Im po rt an t to de ve lo p m et ho ds of th e re se ar ch pr es en ta ti on s to be in te re st in g an d co m pr eh en si bl e to a po li cy -m ak in g au di en ce w.jager@rug.nl 6 Features and Added Value of Simulation Models Using Different Modelling. . . 117 The main advantage of system dynamics models is that they are fast to run and technologically not demanding while providing useful information about the real- world processes and insights into possible impacts of different macro-level policies. However, these models face a number of restrictions. For example, VirSim initially assumes infection probabilities where elderly people (age group 60 and more) have considerably fewer chances of being infected with influenza compared to the other two age groups (Fasth et al. 2010). However, the SEIR model that was applied cannot predict this and cannot explain why this occurs. The authors of VirSim used this result from the micro-simulation model MicroSim and assumed this phenomenon happens because of less social contacts of elderly people or some prior immunity. VirSim cannot explain this phenomenon because system dynamics does not include modelling of various social interactions and other similar dependencies between actors since all variables are averaged over particular groups or the population in general - in the case of VirSim within the members of a particular age group. Apart from the categories of people based on their age, VirSim cannot identify fine-grain groups that have higher probability to be infected. For example, a student has more chances to be exposed and therefore infected than a researcher working in the same university but more in the closed environment of an office while students usually have more frequent social interactions among their groups and communities. It is important to identify closed environments that have high risk of spreading influenza, for example boarding and nursing homes. From the policy modelling point of view, it is important to identify high-risk groups to start the vaccination from there. One could define refined categories of actors by defining more variables, but in general, it would not be possible to represent relations between subcategories, such as taxonomies or ontologies needed to represent social contacts or interactions among actors, due to the lack of representation apparatus in system dynamics models. As Gilbert and Troitzsch argue, due to social complexity and non-linearity, it is difficult to describe processes and systems analytically. To be able to examine interactions between simulation units, other modelling techniques such as ABM or micro-simulation models need to be applied for exploring the social heterogeneity and structures (Gilbert and Troitzsch 2005). Micro-simulation models, usually based on a weighted sum of a representative sample of the population, consider characteristics of individuals and are able to re- produce social reality (Martini and Trivellato 1997). They are beneficial in predicting both, short-term as well as long-term impact of policies (Gilbert and Troitzsch 2005). However, micro-simulation models are costly to build and complex, especially at the level of data analysis requirements. In the case of MicroSim, the Swedish population of approximately nine million people was modelled in many details (Brouwers et al. 2009). In addition, in ‘simple’ cases, especially in demographics, a micro-simulation model produces similar results as a system dynamics-based model (Gilbert and Troitzsch, 2005). This proved true in the case of MicroSim and VirSim: The lat- ter confirmed the results of the former, although with a greater difference between vaccination and non-vaccination results (The National Board of Health and Wel- fare 2011). According to Spielauer, micro-simulation is best to use when population heterogeneity matters; when there are too many possible combinations to split the w.jager@rug.nl 118 D. Majstorovic et al. population into a manageable number of groups; in situations when the micro level explains complex macro-behaviours, or when individual history is important for the model’s outcomes (Spielauer 2011). Although agent-based models lack clear predictive possibilities, they are consid- ered a highly valuable tool for describing and explaining complex social interactions and behaviours, contributing to the understanding of real-world social systems and to a better management of different social processes. Schindler argues that agent-based simulations are capable of representing real-world systems, where small changes in parameter values induce big changes in the model’s outputs. This property shifts attention from the importance of predictions of the system’s future behaviour to the management of critical (social) processes responsible for the changes. However, agent-based simulations alone are not sufficient to model reality. Another possi- ble problem is a high degree of freedom in modelling agents, which amplifies the importance of a proper validation of a simulation model (Schindler 2013). While agent-based and micro-simulation models would be able to show that an elderly person has less infection probability, it is questionable whether they would be able to answer why an elderly person is less infected by influenza. Knowing ‘why’ can help in building a successful strategy for protection against the disease. It might happen that hidden variables and parameters influence this age group. For this reason, in order to model correct probabilities for different age groups, several authors suggest that uncertainty models, such as (dynamic) Bayesian models or Markov chains could be used. In addition, if the past should be also considered (for example, a person has less chances to be infected now because he/she was infected in the recent past), then we have to use more complex probability models, such as the Dempster–Shafer model (Ronald and Halpern 1991, Jameson 1996). Gilbert and Troitzsch argue that statistical models can also be used to predict values of some dependent variables. However, statistical models assume linear relationships between parameters, which becomes a restrictive assumption in the case of (complex) social systems (Gilbert and Troitzsch 2005). Comparing the three different paradigms of social and policy modelling explored in this chapter, the three approaches can be examined according to the level of gran- ularity they are focussing on, the complexity of the models, the demand for the amount of data needed to generate a valuable simulation model and whether social behaviour is modelled. Table 6.8 provides this comparison, which is adapted from (Gilbert and Troitzsch 2005). Micro-simulation models represent particular ontolo- gies of the population or its representative subset based on individuals and are most demanding regarding data needed for developing a model. Agent-based models are less data demanding, less complex and well suited for representing groups of actors (which can represent individuals, groups as well as a system as a whole) and their social behaviour. ABM is the only one of the three paradigms studied which models social behaviour. However, social behaviour cannot be the only source for policy- making (Gilbert and Troitzsch 2005) Macro-models, in this chapter represented by system dynamics, are the least demanding—they model a situation at a global level and require the least data. Nevertheless, they are better for the analysis of short-term policy impacts than for longer-term perspectives (Astolfi et al. 2012). w.jager@rug.nl 6 Features and Added Value of Simulation Models Using Different Modelling. . . 119 Table 6.8 Comparison of simulation modelling theories along level of granularity, the complexity of the models, the amount of data needed to generate simulation models, and the modelling of social behaviour of agents Simulation paradigm Granularity Complexity Data needed Behavioural System dynamics Macro-focusing on the system as a whole Low Aggregated data No Micro-simulation Micro-focusing on individuals High High amount at individual level No Agent-based modelling Micro-macro— focusing on interaction of agents (which can be individuals as well as a system) Medium-high Low to moderate (depending on the number of agents and the policy context) Yes The analysis of three different modelling paradigms with the comparison of five different simulation models has shown that each of the modelling approaches has strengths and weaknesses that constrain their usage in policy-making. For example, micro-simulation can be used for representing social structures while ABM examines interactions between the agents. Astolfi et al argue that none of the theories alone is able to address complex policy interactions (Astolfi et al. 2012). In consequence, a necessary step in the development of simulation modelling is to build and explore ways of maintaining complex simulation models consisting of a few sub-models built on different modelling theories, which communicate with each other by set- ting up and propagating particular parameters after each reasoning iteration (Astolfi et al. 2012). These hybrid models can be considered as modelling platforms or com- plex systems consisting of sub-models. Yet, it is necessary to study methodologies and possibilities of combining different modelling paradigms in order to provide reliable simulation platforms. Current research indicates this trend, as an example of micro-macro combination in a Chronic Disease Prevention Model developed in Australia shows (Brown et al. 2009). However, more research is needed to better understand the implications of combined modelling paradigms, to develop innova- tive simulation platforms that support easy adjustment and development of different models based on different modelling paradigms and to bring evangelists of particular modelling paradigms closer to each other to support mutual understanding and the exploration of the added value and benefits of particular simulation models. Further recommendations and indications of research needs include, but are not exhaustively listed: • Providing guidelines for how to best choose and arrange a collection of smaller (sub) models each describing certain aspects of a given domain of modelling; • Finding the junction points of those models of distinct modelling paradigms with each other by defining input and output parameters for each of the sub-models; • Developing meta-models that reflect the combinatory use of distinct modelling approaches; w.jager@rug.nl 120 D. Majstorovic et al. • Determining the workflow of a simulation process by means of, e.g. a sequence and timing of exchanging the input and output parameters between sub-models in a combined hybrid meta-model; • Exploring more extensive engagement of stakeholders in the policy development9; • Developing more comprehensive simulation platforms that enable the combina- tion of different simulation paradigms in an easy way. 6.5 Conclusions In this chapter, we have examined and compared five different simulation mod- els, which were built on three different modelling paradigms: system dynamics, micro-simulation, and ABM. The chapter first provided an overview of the main char- acteristics of each of the modelling paradigms and then described the five simulation models by outlining them according to a framework elaborated by eGovPoliNet for comparative analysis of knowledge assets. The simulation models are each suitable for representing different aspects of socio-political and/or socio-economic phenom- ena, such as demographic processes (education, social contacts, spread of diseases, etc.), innovation processes or natural resource consumptions (e.g. energy consump- tion). The comparison has revealed the major differences as well as added value and limitations of the different approaches and simulation models. Some lessons from the comparative analysis are that the main strengths of using simulation models in policy- making are the possibilities of exploring and creating understanding of real-world systems and relationships, of experimenting with new situations and of forecasting outputs of alternative policy options or situations based on the given values of pa- rameters. Another key added value of simulation models in policy-making is that simulation models enable the exploration of social processes to evaluate potential impacts of alternative policy options on real-world situations and thus to identify the most suitable policy option. Current paradigms of policy modelling using simulation models are however constrained by their particular focus. Yet, our real-world systems and social processes are complex and require the consideration of parameters at different levels: macro- level, micro-level as well as social behaviour and interconnections between actors. Accordingly, applying one singular approach to modelling a real-world problem is constrained by the particular modelling approach it focuses on: A simulation model of system dynamics may therefore lack precision and social interactions because the missing factors are not accounted for. While the demand for meeting the appropriate level of details included in a model’s description, being not too complex and also not too simple, determines the success of a simulation model, there is a rising need for integrating and combining different modelling paradigms to accommodate the diverse aspects to be considered in complex social world policy contexts. Unifying 9 A more detailed discussion of stakeholder engagement in policy making is given in (Helbig et al. 2014). w.jager@rug.nl 6 Features and Added Value of Simulation Models Using Different Modelling. . . 121 different modelling theories under an umbrella of comprehensive policy modelling platforms is a research need identified in this chapter. Such research should put forward a meta-model of how individual simulation paradigms can be combined, and suggestions of ‘clever’junctions of individual smaller (and self-contained) simulation models dedicated to individual aspects to be modelled. While this chapter selected three widely used simulation paradigms for the study, it does not claim to be exhaustive nor comprehensive. Further research is needed to ex- tend the study to involve other important modelling approaches such as theory-based macro-economic forecasting for instance Dynamic Stochastic General Equilibrium (DSGE) modelling. DSGE is exemplified by the Global Economy Model (GEM) which provides support in policy analysis to central banks and the International Monetary Fund (IMF) (Bayoumi 2004). This will further add to understanding the scope and limitations of different modelling paradigms, as for example Farmer and Foley argue, too, that instead of DSGE models, agent-based models should be used to model the world economy (Farmer and Foley 2009). 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Berlin, Springer Wimmer MA, Scherer S, Moss S, Bicking M (2012) Method and tools to support stake- holder engagement in policy development. The OCOPOMO project. Int J Electron Gov Res 8(3):98–119 Zaidi A, Harding A, Williamson P (2009) New frontiers in microsimulation modeling (Public policy and social welfare. Ashgate Publishing Ltd, England Zeigler B (1976) Theory of modeling and simulation. Wiley, New York w.jager@rug.nl Chapter 7 A Comparative Analysis of Tools and Technologies for Policy Making Eleni Kamateri, Eleni Panopoulou, Efthimios Tambouris, Konstantinos Tarabanis, Adegboyega Ojo, Deirdre Lee and David Price Abstract Latest advancements in information and communication technologies of- fer great opportunities for modernising policy making, i.e. increasing its efficiency, bringing it closer to all relevant actors, and enhancing its transparency and acceptance levels. In this context, this chapter aims to present, analyse, and discuss emerging information and communication technologies (ICT) tools and technologies present- ing the potential to enhance policy making. The methodological approach includes the searching and identification of relevant tools and technologies, their system- atic analysis and categorisation, and finally a discussion of potential usage and recommendations for enhancing policy making. E. Kamateri (�) · E. Panopoulou · E. Tambouris · K. Tarabanis Information Technologies Institute, Centre for Research & Technology—Hellas, Thessaloniki, Greece e-mail: ekamater@iti.gr E. Panopoulou e-mail: epanopou@iti.gr E. Tambouris · K. Tarabanis University of Macedonia, Thessaloniki, Greece e-mail: tambouris@iti.gr, tambouris@uom.gr K. Tarabanis e-mail: kat@iti.gr, kat@uom.gr A. Ojo · D. Lee INSIGHT Centre for Data Analytics, NUIG, Galway, Ireland e-mail: adegboyega.ojo@deri.org D. Lee e-mail: deirdre.lee@deri.org D. Price Thoughtgraph Ltd, Somerset, UK e-mail: david@debategraph.org © Springer International Publishing Switzerland 2015 125 M. Janssen et al. (eds.), Policy Practice and Digital Science, Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_7 w.jager@rug.nl 126 E. Kamateri et al. 7.1 Introduction Policy making may be defined as “the process by which governments translate their political vision into programmes and actions to deliver ‘outcomes’ desired changes in the real world” (UK Government 1999). Policy making encompasses any activity relevant to discussing political issues, identifying areas of improvement or solutions, creating and implementing laws and regulations, monitoring and evaluating current policies, etc. Policy making is a multidisciplinary scientific field referring mainly to politi- cal science, but it may also refer to social, economics, statistics, information, and computer sciences. These diverse scientific fields are essential in order to perform policy making in a more effective and informed manner. Information and communi- cation technologies (ICTs), specifically, have supported decision-making processes for many years. However, the current ICT advancements and good practices of- fer even greater opportunities for modernising policy making, i.e. increasing its efficiency, bringing it closer to all relevant actors and increasing participation, facil- itating its internal processes (e.g. decision making), and enhancing its transparency and acceptance levels. In this context, this chapter aims to present, analyse, and discuss emerging ICT tools and technologies presenting the potential to enhance policy making. Our ap- proach includes searching and identification of relevant tools and technologies, their systematic analysis and categorisation, and finally a discussion of potential usage and recommendations for enhancing policy making. The chapter is structured in the following way: Sect. 7.2 describes our methodological approach, Sect. 7.3 provides the comparative analysis, and Sect. 7.4 discusses the findings and concludes the chapter. Before proceeding to the rest of the chapter, we should provide further clarifica- tions with regard to its scope. First, for work presented in this chapter, policy making is considered as a broad and continuous process that commences from the need to create a policy and ends when a policy is abandoned or replaced. In this context, the policy-making process is usually described with a circular-staged model called “the policy cycle”. There are differences in the number, names, and boundaries of the stages adopted in each proposed policy cycle (e.g. Jann and Wegrich 2006; Northern Ireland Government 2013); however, every policy cycle includes an initiation stage, a drafting stage, an implementation stage, and an evaluation stage. The scope of our work refers to all these stages of the policy cycle. Second, we consider all stakeholders relevant to policy making within the scope of work presented in this chapter. Obviously, the main actor involved in policy making is the government with its different roles, bodies, and institutions. However, noninstitutional actors are also involved such as political parties, political consultants and lobbyists, the media, nongovernmental organisations, civil organisations, and other interested parties depending also on the policy topic at hand. Last but not least, individual citizens are also actors of policy making; as the final policy recipients and beneficiaries, they should actively participate in policy making. Hence, in this w.jager@rug.nl 7 A Comparative Analysis of Tools and Technologies for Policy Making 127 chapter, we do not consider policy making as a close, internal government process, but rather as an open, deliberative process relevant to the whole society. 7.2 Methodology In order to analyse the existing ICT tools and technologies that can be used to enhance the policy-making process, we adopted a simple methodology consisting of four main steps. Before introducing the adopted methodology, we provide a short description with regard to the difference between ICT tools and technologies. ICT tools normally in- clude software applications, web-based environments, and devices that facilitate the way we work, communicate, and solve problems. These are developed by individual software developers, big software providers, researchers, and scientists (Phang and Kankanhalli 2008). Technology, on the other hand, refers to knowledge and know- how, skills, processes, tools and/or practices.1 Therefore, technology not only refers to tools but also the way we employ them to build new things. In the current survey, we organise the findings of our literature analysis based on tool categories. Step 1: Identification During this step, we surveyed the current state of the art to identify ICT tools and technologies that have been (or have a clear potential to be) used to reinforce the policy-making process. These tools have been collected mainly from project deliverables, posts, electronic articles, conference papers, scientific journals, and own contacts and expertise. In particular, we searched for tools and technologies that have been highlighted, used, or created by existing research and coordination projects in the area of e-government and policy modelling, i.e. CROSSOVER2, e-Policy3, FuturICT4, OCOPOMO5, COCKPIT6 and UbiPol7, OurSpace8, PuzzledbyPolicy9, etc. This investigation resulted in a collection of more than 30 ICT tools and technolo- gies mainly coming from project deliverables, posts, electronic articles, conference papers, scientific journals, and own contacts and expertise. Thereafter, we expanded our research on the web to include additional tools that were not previously identified. To this end, we tried multiple searches in the major research databases of computer science, e.g. Association for Computing Machin- ery (ACM) Digital Library and Google Scholar using a combination of different 1 http://en.wikipedia.org/wiki/Technology. 2 http://crossover-project.eu. 3 http://www.epolicy-project.eu/node. 4 http://www.futurict.eu. 5 http://www.ocopomo.eu. 6 http://www.cockpit-project.eu. 7 http://www.ubipol.eu/. 8 http://www.ep-ourspace.eu/. 9 http://www.puzzledbypolicy.eu/. w.jager@rug.nl 128 E. Kamateri et al. keywords such as tools, technologies, policy modelling, online participation, en- gagement, government, policy making, decision making, policy formulation, etc. The references of the selected papers were checked and additional papers were found. Some of the journals that have been reviewed include Government Infor- mation Quarterly, International Journal of Electronic Government Research. In addition, we surveyed similar initiatives that summarise tools or/and methods, i.e. the Participation Compass10 launched by Involve11 (not-for-profit organisation in public participation), the ParticipateDB12 by Intellitics13, and the ReformCompass by Bertelsmann Stiftung14 (providers of digital engagement solutions). The final result of this exercise was a list of 75 tools and technologies. Step 2: Categorisation Analysing the identified tools and technologies, it was ev- ident that most of them fall under a number of categories. We defined, therefore, 11 categories of tools and technologies for policy making. Each category has a spe- cific application focus, e.g. opinion mining, serious games, etc., and may be further divided into one or more subcategories. We then organised tools and technologies’ analysis according to the defined cate- gories. There are few cases, however, where the same tool could be classified under more than one category, i.e. in the case of visualisation and argumentation tools and in the case of serious games and simulation tools. In the first case, argumentation tools represent and structure arguments and debates, and usually exploit visual means in order to clearly represent the arguments. However, the main focus remains the representation of arguments. On the other hand, the visualisation tools present, in a graphical form, any type of input data. Thus, it was selected for the sake of simplicity to analyse each tool in one category according to its most prominent feature. Similar difficulties in categorisation have also arisen in the case of simulation tools and seri- ous games. Serious games are created for educational and entertainment purposes, or for helping citizens to further understand some processes by playing the role of a key stakeholder. On the other hand, simulation tools are usually created on a more serious context (e.g. within a research project, taking into account accurate real-world data) in order to help real policy makers or governments to simulate long-term impacts of their actions. Therefore, the categorisation of tools in these two categories was made based on the context and the goal of the tool. Step 3: Comparative Analysis A comparative analysis of identified ICT tools and technologies per category was then performed. Initially, we analysed tools’ function- ality to identify core capabilities per category. Then, we examined the key features for each tool. The outcome of this analysis is a comparative table for each category 10 http://participationcompass.org/. 11 http://www.involve.org.uk. 12 http://participatedb.com/. 13 http://www.intellitics.com/. 14 http://www.reformkompass.de. w.jager@rug.nl 7 A Comparative Analysis of Tools and Technologies for Policy Making 129 that shows, at a glance, an overview of different features found in each tool of the category. Step 4: Conceptualisation During this step, we performed an overall discussion of the presented tools and technologies and their potential for enhancing policy making. To this end, we examined three main aspects for policy making—the type of facilitated activities, the type of targeted stakeholders, and the stages of the policy cycle. Finally, we drafted overall recommendations and conclusions. 7.3 Tools and Technologies for Policy Making Based on the literature survey, we identified 11 main categories of ICT tools and technologies that can be used for policy making purposes as follows: • Visualisation tools help users better understand data and provide a more meaningful view in context, especially by presenting data in a graphical form. • Argumentation tools visualise the structure of complex argumentations and debates as a graphical network. • eParticipation tools support the active engagement of citizens in social and political processes including, e.g. voting advice applications and deliberation tools. • Opinion mining tools help analyse and make sense of thousands of public comments written in different application contexts. • Simulation tools represent a real-world system or phenomenon and help users understand the system and the effects of potential actions in order to make better decisions. • Serious games train users through simulation and virtual environments. • Tools specifically developed for policy makers have been recently developed to facilitate the design and delivery of policies. • Persuasive tools aim to change users’ attitudes or behaviours. • Social network analysis (SNA) tools analyse social connections and identify patterns that can be used to predict users’ behaviour. • Big data analytics tools support the entire big data exploitation process from discovering and preparing data sources, to integration, visualisation, analysis, and prediction. • Semantics and linked data tools enable large amounts of data to become easily published, linked to other external datasets, and analysed. We present an analysis of each category of tools and technologies in the rest of this section.15 15 All tools mentioned in this section are summarized in the end of the chapter along with their links. w.jager@rug.nl 130 E. Kamateri et al. 7.3.1 Visualisation Tools Visualisation tools enable large amounts of “raw” data to become visually represented in an interpretable form. Moreover, they provide appropriate means to uncover pat- terns, relationships, and observations that would not be apparent from looking at it in a nonvisual format. Therefore, users can explore, analyse, and make sense of data that, otherwise, may be of limited value (Osimo and Mureddu 2012). Today, there are many data visualisation tools, desktop- or web-based, free or proprietary, that can be used to visualise and analyse raw data provided by the user. Examples include Google Charts, Visokio Omniscope, R, and Visualize Free. Besides visual presentation and exploration of raw data, they provide additional features such as data annotation (e.g. Visokio Omniscope), data handling, and other statistical computations on raw data (e.g. R). Over recent years, geovisualisation (shortened form of the term geographic vi- sualisation) has gained considerable momentum within the fields of geographic information systems (GIS), cartography, and spatial statistics. Some consider it to be a branch of data visualisation (Chang 2010). However, geovisualisation inte- grates different approaches including data visualisation, such as cartography, GIS, image analysis, exploratory data analysis, and dynamic animations, to provide visual exploration, analysis, synthesis, and presentation of geospatial data (MacEachren and Kraak 2001). Geovisualisation tools have been widely used to visualise societal statistics in combination with geographic data. Several visualisation and geovisualisation tools have been developed to visualise and analyse demographic and social statistics in several countries across the world. Most tools are used for data coming from the USA. However, many efforts have been made, lately, to visualise statistics coming from all over the world (e.g. Google Public Data Explorer and World Bank eAtlas). The most important source of information for these tools is governmental reports which are made available by each state. Most tools support data transparency, mainly for downloading data and figures, while uploading of users’ data is available only in few cases. Visualisation tools are organised into static and interactive based on a categorisation proposed for web- mapping tools (Kraak and Brown 2001). A static tool contains a figure or a map displayed as a static image (Mitchell 2005), while interactive tools allow users to access a set of functions to have some interaction with the tool or the map, such as zooming in and out (Mitchell 2005). Table 7.1 summarises well-known visualisation and geovisualisation tools and compares their main characteristics. In particular, the table provides information on: (a) the number and subject of indicators, e.g. if they deal with demographic, health, environmental, or other social issues, (b) the coverage, namely, the countries supported, (c) the period for which statistics are available, (d) data transparency, and (e) whether it is a static or interactive tool. w.jager@rug.nl 7 A Comparative Analysis of Tools and Technologies for Policy Making 131 Table 7.1 Visualisation and geovisualisation tools for analyzing regional statistics Indicators and Topic Coverage (countries) Period Data transparency Static/ Interactive Gapminder > 400
Demographics,
social,
economic, en-
vironmental,
health
> 200 Over the past
200 years
Download
and upload
Interactive
Worldmapper ∼ 696 maps
Demographics
All N/A Download
(No custom
maps)
Datasets,
static
Dynamic
Choropleth
Maps
Multiple
social,
economic,
and environ-
mental
USA N/A Download
(free to adjust
the threshold
criteria)
Interactive
DataPlace ∼ 2360
Demographics,
health, arts,
real estate
USA After 1990 N/A Interactive
Data
Visualizer-
World
Bank
∼ 49
Social,
economic,
financial, IT,
and environ-
mental
209 1960–2007 N/A Interactive
World Bank
eAtlas
∼ 175
Development
challenges
200 After 1960 Download
and upload
Interactive
State
Cancer
Profiles
Demographic
data related to
cancer
USA 2006–2010 N/A Interactive
Health
Infoscape
Health
conditions
USA January 2005–
July 2010
N/A Interactive
OECD
eXplorer
∼ 40
Demographics,
economic,
labour
market,
environment,
social, and
innovation
34
(335 large
regions
1679 small
regions)
1990–2005 Download
and upload
Interactive
(time
animation)
storytelling
Other tools investigated, but not included, in the above table include STATcompiler, Google
Public Data Explorer, NComVA, Social Explorer (USA), PolicyMap (USA), All-Island Research
Observatory (UK), and China Geo-Explorer II
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132 E. Kamateri et al.
Demographic, social, environmental, health, and other public data, provided by
governmental and public authorities, in raw form, can be transformed and presented
through visualisation and geovisualisation tools into a more interpretable way. Thus,
information and current trends hidden in this data can easily become apparent. This
can assist policy stakeholders and decision makers to make more informed decisions.
In addition, incorporating geographical knowledge into planning and formation of
social and political policies can help derive more accurate spatial decisions. Obvious
fields where visualisation and geovisualisation tools can be applied for policy making
are investment, population, housing, environmental assessment, public health, etc.
7.3.2 Argumentation Tools
Argumentation tools visualise the structure of complex arguments and debates as a
graphical network. In particular, they allow a large number of stakeholders to partici-
pate, discuss, and contribute creative arguments and suggestions which then become
visualised. This visual representation provides a better and deeper understanding of
topics discussed. Thus, complex debates can become easily analysed, refined, or
evaluated, e.g. by pinpointing possible gaps and inconsistencies or strong and weak
points in the arguments, etc. (Benn and Macintosh 2011).
Table 7.2 summarises well-known argumentation tools and depicts their main
characteristics (i.e. whether they are open source, whether they enable import-
ing/exporting data, whether they are Web-based or collaborative, the argument
framework, whether they support visual representation argumentation structure mod-
ification and manipulation of layouts). DebateGraph, Rationale, Cope It!, and
bCisive constitute proprietary solutions, while Cohere, Araucaria, Compendium,
and Carneades were developed during research studies within universities and re-
search projects. Most argumentation tools enable users to share ideas and collaborate
upon “wicked problems”. For example, DebateGraph allows users to collaboratively
modify the structure and the content of debate maps in the same way they can
collaboratively edit a wiki. In addition, MindMeister and Compendium constitute
desktop-based solutions that support collaborative argument analysis, while Mind-
Meister and bCisive also enable real-time collaboration. Though most argumentation
tools provide, even partially, a visual representation of discussions, only few sup-
port an easy layout manipulation; such tools are Compendium, Araucaria, Cohere,
and DebateGraph. Besides argument analysis, argumentation tools offer additional
features, such as argument reconstruction, discussion forums, argument evaluation,
etc. For example, Araucaria and Argunet enable users to reconstruct and map de-
bates, Cohere enables any content on the web to serve as a node of information
in the argument map, and Rationale allows users to judge the strength of an argu-
ment by evaluating its elements. These judgments are also represented on the map.
Similarly, Carneades allows users to evaluate and compare arguments as well as to
apply proof standards. Finally, Cope It! supports a threaded discussion forum, while
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7 A Comparative Analysis of Tools and Technologies for Policy Making 133
Table 7.2 Argumentation tools. (Source: Benn and Macintosh 2011)
Tool Open
source
Import/
export
Web-
based
Collab-
orative
Argument
frame-
work
Visual
represen-
tation
Modify
argument
structure
Manipulate
layouts
Araucaria Yes Yes No No Walton,
Toulmin,
Wigmore,
Classical
Partially Yes Partially
Argunet Yes Yes Yes Yes Classical Yes Partially N/A
Carneades Yes Yes Yes No Walton Partially Yes N/A
Cohere Yes Yes Yes Yes IBIS Yes Partially Partially
Compendium Yes Yes No Yes IBIS Yes Partially Partially
Cope_it! N/A No Yes Yes IBIS Yes Partially N/A
DebateGraph No No Yes Yes Multiple
(including
IBIS)
Partially Partially Partially
Rationale No No No No Classical Partially Partially N/A
bCisive No No Yes Yes IBIS Partially Partially N/A
MindMeister No Yes Yes Yes N/A Yes Yes Partially
IBIS Issue-Based Information System
bCisive incorporates group planning, decision making, and team problem-solving
capabilities.
Argumentation tools facilitate better-informed public debate, policy deliberation,
and dialogue mapping on the web about complex political issues. For example,
DebateGraph has been used by the Dutch Foreign Ministry in its recent consultation
on its human rights policy16, the UK Prime Minister’s Office17, and the White
House’s Open Government Brainstorming.18 Compendium has been used in a case
study for consultation on regional planning in southeast Queensland (Ohl 2008).
Carneades has been developed during the European Estrella project19 that aims to
help both citizens and government officials to take part, more effectively, in dialogues
for assessing claims and has been used in several applications.
16 http://debategraph.org/MR
17 http://debategraph.org/No10.
18 http://debategraph.org/WH.
19 http://www.estrellaproject.org/.
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134 E. Kamateri et al.
7.3.3 eParticipation Tools
eParticipation tools have been specifically developed to involve citizens in the policy-
making process, i.e. to enable citizens to get informed, to provide feedback on
different policy issues, and to get actively involved in decision making (Gramberger
2001). These tools are mainly based on Web 2.0 features including a variety of social
networking tools such as discussion forums or message boards, wikis, electronic
surveys or polls, e-petitions, online focus groups, and webcasting.
eParticipation may entail different types of involvement, which are supported
by different tools and functionalities, ranging from the provision of information,
to deliberation, community building and collaboration, active involvement through
consultations, polling, and decision making. The International Association for Public
Participation (IAP2) has produced a public participation spectrum20, which shows
how various techniques may be employed to increase the level of public impact.
Recently, eParticipation tools have been widely used by governmental and public
authorities. Through actively engaging citizens, in the planning, design, and de-
livery process of public policies, they have moved towards improving democratic
governance, preventing conflicts, and facilitating citizens’ active participation in
the solution of issues affecting their lives. Table 7.3 presents a set of such recently
developed eParticipation tools.
7.3.4 Opinion Mining Tools
The Web’s widespread use over the past decade has significantly increased the pos-
sibility for users to express their opinion. The users not only can post text messages
now but also can see what other users have written about the same subject in a variety
of communication channels across the Web. Moreover, with the advent of Twitter
and Facebook, status updates, and posts about any subject have become the new
norm in social networking. This user-generated content usually contains relevant
information on the general sentiment of users concerning different topics including
persons, products, institutions, or even governmental policies. Thus, an invaluable,
yet scattered, source of public opinion has quickly become available.
Opinion-mining tools (or otherwise called sentiment analysis tools) perform a
computational study of large quantities of textual contributions in order to gather,
identify, extract, and determine the attitude expressed in them. This attitude may be
users’ judgment or evaluation, their affectual state (that is to say, the emotional state
of the author when writing), or the intended emotional communication (that is to say,
the emotional effect the author wishes to have on the reader; Stylios et al. 2010).
20 Available at: http://www.iap2.org/associations/4748/files/spectrum .
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7 A Comparative Analysis of Tools and Technologies for Policy Making 135
Table 7.3 eParticipation tools developed to improve people involvement in government
Typical actions Examples
Citizen Space Consultation and engagement software
Create, organise, and publish public
consultations across the net on complex
policy documents
Share consultation data openly in a
structured way
Provide a way to easily analyse consultation
data (both qualitative and quantitative)
Used by government bodies
to run e-consultations
around the world
Adhocracy.de Participation and voting software
Present and discuss issues
Collaborate (develop and work on texts
together)
Make proposals, gather, and evaluate
proposals
Add polls for decision making
Vote on issues
Used in the Munich Open
Government Day where
citizens could propose
policies, projects, and
actions of the city
MixedInk.com Collaborative writing software
Large groups of people work together to
write texts that express collective opinions
Post ideas
Combine ideas to make new versions
Post comments and rate versions to bring the
best ideas to the top
Used by the White House to
let citizens draft collective
policy recommendations for
the Open Government
Directive
Loomio.org Decision making and collaborative software
Initiate discussions and present proposals
that can then be discussed, modified, and
voted (Agree, Abstain, Disagree, or Block,
along with a brief explanation of why)
Change their position any time
Used by the Wellington City
Council for discussion with
their citizens
CitySourced Mobile civil engagement platform
Identify and report civic issues (graffiti,
trash, potholes, etc.), and comment on
existing ones
Used in San Francisco, Los
Angeles, and several other
cities in California
Puzzledbypolicy Consultation and opinion mapping software
Learn about policy issues concerning
immigration in the European Union (EU)
Give their voice
Graphically compare their views on
immigration with national and EU
immigration policies as well as with the
opinions of relevant stakeholders
Encourage to join discussions on particular
aspects of immigration policy they feel
strongly about
Used by the Athens and
Torino municipalities and
other stakeholders in
Tenerife, Hungary, and
Slovenia
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136 E. Kamateri et al.
Table 7.3 (continued)
Typical actions Examples
Opinion Space Opinion mapping software
Collect and visualise user opinions on
important issues and policies (rate five
propositions on the chosen topic and type
initial response to a discussion question)
Show in a graphical “map” where user’s
opinions fall next to the opinions of other
participants
Display patterns, trends, and insights
Employ the wisdom of crowds to identify the
most insightful ideas
Used by US State
Department to engage
global online audiences on a
variety of foreign policy
issues
CivicEvolution.org Collaboration platform
Engage citizens in structured dialogue and
deliberation and develop detailed
community-written proposals to make
constructive changes
Used by the City of Greater
Geraldton, in Australia, to
facilitate collaboration and
deliberation among
participants in participatory
budgeting community
panels
UbiPol Mobile civil engagement platform
Identify and report problems or suggestions
Report policy issues
Used by TURKSAT, a
publicly owned but privately
operated company in
affiliation with Ministry of
Transportation in Turkey
OurSpace Youth eParticipation platform
Engage young people in the decision-making
process
Enable collaboration
European and National
Youth Organisations already
using OurSpace
Dialogue App Set up a dialogue
Share, rate, comment, and discuss ideas and
bring the best ideas to the top
Department for
Environment, Food and
Rural Affairs in the UK is
using Dialogue App to get
thoughts, ideas and input on
how to improve and
formulate policy
In social media, opinion mining usually refers to the extraction of sentiments from
unstructured text. The recognised sentiments are classified as positive, negative, and
neutral, or of a more fine-grained sentiment classification scheme. Examples include
Sentiment140, Sentimentor, Repustate, etc. Opinion-mining tools may also integrate
a broad area of approaches including natural language processing, computational
linguistics, and text mining. Text mining, for example, can provide a deeper analysis
of contributions; it summarises contributions, helps highlight areas of agreement and
disagreement, and identifies participants’ main concerns—the level of support for
draft proposals or suggestions for action that seem necessary to address. Opinion
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7 A Comparative Analysis of Tools and Technologies for Policy Making 137
mining tools providing such approaches include DiscoverText, RapidMiner, and
Weka.
Classifying statements is a common problem in opinion mining, and different
techniques have been used to address this problem. These techniques follow two
main approaches; those based on lexical resources and neutral-language processing
(lexicon-based) and those employing machine-learning algorithms. Lexicon-based
approaches rely on a sentiment lexicon—a collection of known and precompiled
sentiment/opinion terms. These terms are words that are commonly used to ex-
press positive or negative sentiments, e.g. “excellent”, “great”, “poor”, and “bad”.
The method basically counts the number of positive and negative terms, and de-
cides accordingly the final sentiment. Machine-learning approaches that make use
of syntactic and/or linguistic features and hybrid techniques are very common, with
sentiment lexicons playing a key role in the majority of methods.
Table 7.4 presents several opinion mining tools that have been recently developed
to analyse public opinions.
Opinion mining tools can help derive different inferences on quality control, pub-
lic relations, reputation management, policy, strategy, etc. Therefore, opinion mining
tools can be used to assist policy stakeholders and decision makers in making more in-
formed decisions. In particular, knowing citizens’ opinion about public and political
issues, proposed government actions, and interventions or policies under formation
can ensure more socially acceptable policies and decisions. Finally, gathering and
analysing public opinion can enable us to understand how a certain community re-
acts to certain events and even try to discover patterns and predict their reactions to
upcoming events based on their behaviour history (Maragoudakis et al. 2011).
7.3.5 Simulation Tools
Simulation tools are based on agent-based modelling. This is a recent technique
that is used to model and reproduce complex systems. An agent-based system is
formed by a set of interacting and autonomous “agents” (Macal and North 2005) that
represent humans. Agents act and interact with their environment, including other
agents, to achieve their objectives (Onggo 2010). Agents’ behaviour is described by
a set of simple rules. However, agents may also influence each other, learn from
their experiences, and adapt their behaviour to be better suited to their environment.
Above all, they operate autonomously, meaning that they decide whether or not to
perform an operation, taking into account their goals and priorities, as well as the
known context. The analysis of interactions between agents results at the creation
of patterns that enable visualising and understanding the system or the phenomenon
under investigation.
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138 E. Kamateri et al.
Table 7.4 Opinion mining tools
Purpose Sources Classification
SwiftRiver Aggregate, manage,
filter, and validate
web data
Discover relationship
and trends in data
Twitter, SMS, e-mail,
and RSS feeds
Machine learning
DiscoverText (Text analytics)
Search, filter, collect,
and classify data
Generate insights
E-mail archives,
social media content,
and other document
collections
Machine learning
Repustate Categorise and
visualise social media
data
Extract text sentiment
Predict future trends
Twitter or Facebook
Multiple languages
Machine learning
Opinion observer (Opinion mining)
Extract text sentiment
Discover patterns
Web pages Lexicon-based
(feature category)
AIRC Sentiment
Analyser
Extract text sentiment N/A Lexicon-based
Social Mention Aggregate and analyse
social media data
Extract text sentiment
Discover patterns
Blogs, comments,
social media including
Twitter, Facebook,
Social bookmarks,
microblogging
services, Images,
News, etc.
Lexicon-based
Umigon Sentiment analysis Twitter Lexicon-based
Convey API Sentiment analysis Social media records Machine learning
Natural-language
processing
Statistical modelling
Sentiment140 Sentiment analysis for
tweets on a subject or
keyword
Twitter Machine learning
Natural-language
processing
Sentimentor Sentiment analysis for
tweets on a subject
Twitter Machine learning
Corpora’s Applied
Linguistics
Document
summarisation and
sentiment analysis
Documents Natural-language
processing in
combination with an
extensive English
language lexicon
Attentio Sentiment analysis Blogs, news, and
discussion forum sites
Lexicon-based
Machine learning
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7 A Comparative Analysis of Tools and Technologies for Policy Making 139
Table 7.4 (continued)
Purpose Sources Classification
Opinmind Sentiment analysis of
bloggers opinion
Blogs Not available
ThinkUp Archive and analyse
social media life
Twitter and Facebook Machine learning
In this sense, simulation tools are particularly suited to explore the complexity of
social systems. A social system consists of a collection of individuals who interact
directly or through their social environment. These individuals evolve autonomously
as they are motivated by their own beliefs and personal goals, as well as the cir-
cumstances of their social environment. Simulating social systems and analysing the
effects of individuals’ interactions can result in the construction of social patterns
(e.g. how society responds to a change) that can be used for policy analysis and
planning as well as for participatory modelling (Bandini et al. 2009).
There are several general-purpose simulation tools. Most of them are open source
and free to be accessed by anyone. Some of these are specially designed to focus
on social systems. For example, Multi-Agent Simulation Suite (MASS) is a soft-
ware package intended to enable modellers to simulate and study complex social
environments. To this end, it models the individual together with its imperfections
(e.g. limited cognitive or computational abilities), its idiosyncrasies, and personal
interactions. Another tool focusing on the development of flexible models for living
social agents is Repast.
An increasing number of tools for the simulation and analysis of social inter-
actions has been developed in recent years. These aim to help policy stakeholders
and decision makers to simulate the long-term impact of policy decisions. Table 7.5
presents such simulation tools that have been used in the field of health, environment,
developmental policies, etc.
7.3.6 Serious Games
Agent-based modelling is used also in serious games, providing the opportunity for
experiential and interactive learning and exploration of large uncertainties, divergent
values, and complex situations through an engaging, active, and critical environment
(Raybourn et al. 2005). Serious games enable players to learn from the accurate rep-
resentations of real-world phenomena and the contextual information and knowledge
and data embedded in the dynamics of the game. Abt (1987) defines serious games
as games with “an explicit and carefully thought-out educational purpose and are not
intended to be played primarily for amusement”.
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140 E. Kamateri et al.
Table 7.5 Simulation tools simulating the long-term impact of policy decisions
Purpose Input Interface Scale
Threshold 21 Simulate the
long-term impact of
socioeconomic
development
policies
About 800 variables
concerning
economic, social,
and environment
factors
Flexible Customisable
to suit the
needs of any
sector and
country
GLEaMviz Simulate global
epidemics
Detailed
population,
mobility, and
epidemic–infection
data (real-world
data)
Compartmentalised
disease models
Visual tool
for designing
compartmen-
tal
models
Thirty
countries in 5
different
continents
The Climate
Rapid
Overview and
Decision-
support
Simulator
(C-ROADS)
Simulate long-term
climate impacts of
policy scenarios to
reduce greenhouse
gas emissions (CO2
concentration,
temperature,
sea-level rise)
Sources of
historical data
Flexible
equations are
available and
easily
auditable
Six-region and
15-region
mode
UrbanSim Simulate the
possible long-term
effects of different
policies on urban
development
(land use,
transportation, and
environmental
planning)
Historical data Flexible Any country
Modelling the
Early Life
Course
(MEL-C)
Simulate the effects
of policy making in
the early life course
and issues
concerning children
and young people
Data from existing
longitudinal studies
to quantify the
underlying
determinants of
progress in the
early life course
Flexibly
adapted for
new data and
parameter
inputs
N/A
Global
Buildings
Performance
Network
(GBPN)
Policy
Comparative
Tool
An interactive tool
that enables users to
compare the
world’s best
practice policies for
new buildings
(residential and
commercial)
N/A N/A N/A
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7 A Comparative Analysis of Tools and Technologies for Policy Making 141
Table 7.5 (continued)
Purpose Input Interface Scale
CLASP’s
Policy
Analysis
Modeling
System
(PAMS)
Forecast the
impacts of energy
efficiency standards
and labelling
programs
Assess the benefits
of policies, identify
the most attractive
targets for
appliances, and
efficiency levels
N/A N/A Support basic
modelling
inputs for
over 150
countries
Customisable
where
country-
specific data
is available
Scenario
Modelling and
Policy
Assessment
Tool
(EUREAPA)
Model the effects of
policies on
environment,
consumption,
industry, and trade
Detailed carbon,
ecological, and
water footprint
indicators
N/A N/A
Budget
simulator
Budget consultation
platform that
enables to adjust
budget items and
see the
consequences of
their allocations on
council tax and
service areas
N/A Flexible Any country
CLASP Comprehensive, Lightweight Application Security Process, EUREAPA, MEL-C, C-ROADS
In policy making, serious games provide the opportunity for players to assume
roles of real-world critical stakeholders whose decisions rely on extensive data col-
lected from the world around them. In this way, players get educated on the process
of decision making as well as on the limitations and trade-offs involved in policy
making. Serious games may be used in fields like defence, education, scientific ex-
ploration, health care, emergency management, city planning, engineering, religion,
and politics (Caird-Daley et al. 2007).
Table 7.6 summarises a number of serious games aiming to tackle different
social and political problems. In some of these, users assume the role of critical
stakeholders. For example, in 2050 Pathways, users play as if they were the Energy
and Climate Change Minister of the UK, while, in Democracy, users act as the
president or the prime minister of a modern country. Other games enable users to
apply policies/strategies and explore their potential impact. Such an example is the
Maryland Budget Map Game that gives the option to make cost-cutting decisions
and consider short-term and long-term budget effects. Serious games also help users
gain virtual experiences for solving real-world problems. Thus, such games could be
used to train citizens and public authorities on how to enforce a policy, e.g. a disas-
ter or crisis management policy. For instance, Breakaway simulates critical incidents
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142 E. Kamateri et al.
Table 7.6 Serious games focusing on policy making
Purpose Features Scope
2050 Pathways Users act as the energy and
climate change minister
and explore the complex
choices and trade-offs
which the UK will have to
make to reach the 80 %
emission reduction targets
by 2050, while matching
energy demand and supply
It covers all parts of
the economy and all
greenhouse gas
emissions
Users create their
emission reduction
pathway, and see the
impact using real
scientific data
Scientific
exploration and
engineering
Democracy Users are in the position of
president (or prime
minister) of a modern
country and the objective
of the game is to stay in
power as long as possible
It recreates a modern
political system as
accurately as possible
Users influence the
voters and the
country by putting in
place policies
Education,
political strategy
Maryland Budget
Map Game
Users act as the
administration and general
assembly of a state
Gives the options to make
cost-cutting decisions,
weigh revenue options,
and consider short-term
and long-term budget
effects
It explains how
budgeting decisions
are made
Education,
political strategy
NationStates—
create your own
country
Users build a nation and
run it according to their
political ideals and care
for people
N/A Entertainment,
education
Breakaway(disaster
management—
incident
commander)
Helps incident
commanders and other
public safety personnel
train and plan for how they
might respond to a wide
range of critical incidents
It models acts of
terrorism, school
hostage situations,
and natural disasters
Education,
emergency
management
The Social
Simulator
Trains communications,
policy, and frontline staff
in a variety of sectors
using a number of crisis
scenarios
Users use the language,
tools, and norms of the
social web for crisis
response
It models terrorist
attacks, a leaked
report spreads anger
about a government
policy, etc.
Education,
emergency
management,
political strategy
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7 A Comparative Analysis of Tools and Technologies for Policy Making 143
Table 7.6 (continued)
Purpose Features Scope
CItyOne Users are poised with a
series of problems
concerning energy, water,
or commercial
investments (such as
banking and payment
systems) and are asked to
address specific challenges
Planner players think
through the sorts of
energy, water, or
commercial
investments that
might be needed for
particular urban
environments in the
years to come
Education,
awareness
World Without
Oil
Engages people concerned
with the world’s
dependence on oil and
both educate and move
them to action and
contribute “collective
imagination”
Risks that oil
extraction poses to
our economy,
climate, and quality
of life
Awareness, public
good
Urgent Evoke Empowers people all over
the world to come up with
creative solutions to the
most urgent social
problems
N/A Awareness
MP For A Week Enables users to learn
about the work of a
member of parliament
(MP) and key features of
democracy in the UK
N/A Education
Budget Hero Allows players to build a
balanced budget
Creates and tests a
budget policy and
sees the effects of
those cuts or
increased expenses
on the federal budget
Education, political
strategy
and risk scenarios and helps players train and plan their responses. Last, they improve
imaginary thinking by exploring possible futures and sparking future-changing ac-
tions. For example, Urgent Evoke invites people to come up with creative solutions
to the most urgent social problems. Other games focusing on a better world can be
found in World-Changing Game21 and Purposeful Games22; however, they are not
included in Table 7.6 due to their loose connection with the policy-making process.
21 http://www.scoop.it/t/world-changing-games.
22 http://purposefulgames.info/.
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144 E. Kamateri et al.
Table 7.7 Political analysis tools
Purpose Features Scope
PolicyMaker Helps users to analyse,
understand, and create
effective strategies to
promote point of view on
any policy question or
political issue
Conduct a stakeholder
analysis
Identify political dynamics
of policy making
Analyse systematically the
supporters, why a policy may
face opposition, and what
strategies might help it be
more effective
Design political strategies to
support a policy
Policy
planning
Oracle Policy
Automation
for Social
Services
Transform complex policies
in human language
Assess impact of policy
changes by enabling what-if
analysis of proposed
amendments
It includes debugging,
regression testing, policy
simulation, and what-if
analysis for policy changes
Policy
delivery
7.3.7 Tools Specifically Developed for Policy Makers
Policy-making tools are designed to facilitate governments, industry, construction
experts, and other stakeholders design and deliver national renovation policies and
strategies. We present two illustrative examples of such tools in Table 7.7.
7.3.8 Persuasive Tools
Persuasive tools aim to change users’ attitudes or behaviours, such as exercising more
or sticking to medication, by enhancing feedback, persuasion and social influence,
but not through coercion (Fogg 2002). Persuasive tools can be applied in policy
making for promoting different political causes and enhancing policies’ adoption by
the public. The Behavioural Insights Team has published a paper on fraud, debt,
and error that presents a completely new way of doing policy based on citizens’
behavioural reactions (Behavioural Insights Teem; BIT 2012).
Until recently, only indirect efforts have been made to persuade or motivate
citizens adopting a specific policy. For example, the USA23 and Australia24 have
developed smartphone applications that enable taxpayers to keep up to date with
their tax affairs. In addition, the Australian Tax Office offers a “Tax Receipt Log”
app that makes it easier to keep up to date on expenses and tax receipts by using the
23 http://www.irs.gov/uac/New-IRS2Go-Offers-Three-More-Features.
24 http://www.taxreceiptlog.com/blog/gst/tax-calculator/.
w.jager@rug.nl

7 A Comparative Analysis of Tools and Technologies for Policy Making 145
phone camera to take a photo of a receipt, which is then processed and stored. These
can serve as persuasive tools reminding citizens of their delayed fees or motivating
them to ask for receipts for their purchases.25
7.3.9 Social Network Analysis Tools
A social network consists of nodes representing individual actors within the network
and ties which represent relationships between the individuals. Social network analy-
sis (SNA) tools facilitate the study of social structure, providing the means (methods)
to determine if there are regular patterns in social relationships, and how these pat-
terns may be related to attributes or behaviour (Tang et al. 2011). In addition, SNA
could identify and map informal networks around any given issue. It can be used to
identify who is connected to whom and thus adds value/does not add value, and who
should be connected to whom to solve the issue at hand. It also identifies conflicts
and broken links that need attention to facilitate more functional action-orientated
relationships to achieve goals (Rowena 2010).
SNA can be used in policy making in order to identify a social network’s patterns
and key actors and try to influence these (and, therefore, their networks) by applying
appropriate targeted policy interventions. For example, SNA could be used to think
through and tackle social issues such as unemployment. To do this, SNA may pinpoint
the most influential key actors relevant to entrepreneurship or employment (e.g.
pioneering entrepreneurs, venture capitalists, etc.) and target these for promoting
entrepreneurship or employment policies.
Magus Networker26 was designed to illuminate complex, informal networks so
that they become understandable. Powerful querying functions enable key patterns
to be identified quickly, displaying where opportunities for improving performance
can be developed.
7.3.10 Big Data Analytics Tools
Over the last decade, much information has gradually become open. Sources of such
information include machine-operated sensors, video, digital images, e-mail, social
media, and open data from government, research institutes, and nongovernmental
organisations. The aim of open data movement is to make information freely avail-
able, without restrictions and in standard machine readable format (United Nations
Department of Economic 2010).
25 Due to the limited number of the identified tools for this category and the following ones, we
decided not to summarise them in a table format.
26 http://www.magus-toolbox.com/Networker/.
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146 E. Kamateri et al.
Open data create significant opportunities for achieving deeper and faster insights
towards knowledge development, decision making and interdisciplinary collabora-
tion. However, they have little value if people cannot use them. Thus, new tools and
technologies were developed lately to address this problem. One of these technologies
is big data analytics.
Big data analytics tools have emerged due to the increasing volume and variety
of open data that became available on the web. The term big data refers to datasets
so large and complex that are difficult to process using traditional data management
and processing techniques.
Big data analytics tools aim to tackle several technological and analytical chal-
lenges, such as analysing unstructured data, uncovering hidden patterns, exploiting
social media, making fast decisions on massive data volumes, etc. Furthermore, big
data predictive analytics aim to unlock the value of big data and make predictions
about future, or otherwise unknown events, in a near-real-time mode (Nyce 2007).
Big data analytics tools can be used by government agencies for information
purposes, e.g. for understanding what people are saying about government, and
which policies, services, or providers are attracting negative opinions and complaints.
Moreover, they can find out what people are concerned about or looking for, e.g.
from the Google Search application programming interface (API) or Google Trends,
which record Google’s search patterns of a huge number of internet users. Based
on analysis of current and “historical” facts, they can develop accurate models and
forecasts about the future.
In addition, big data can contribute to “smart” cities and governments and to trans-
formational government. In particular, big and open data can foster collaboration;
create real-time solutions to tackle challenges in agriculture, health, transportation,
and more; promote greater openness; and introduce a new era of policy and decision
making (Bertot et al. 2014).
Several applications utilising the power of big data are already available. An
example is the case of an insurance company, named The Climate Corporation,
which examines massive streams of climate data to assess future risk and current
damage and provide insurance to farmers who can lock in profits even in the case of
drought, excessive rains, or other adverse weather conditions.
Despite the wide adoption in the private sector, big data still have limited applica-
tions in policy making. One of the few initiatives is that of New Zealand, which has
recently expressed their intention to reform and/or create new governmental services
to improve people, society, and economy. In particular, the Ministry of Education
in New Zealand is already processing population projections, building consent data,
and school enrolment data to work out where new schools are needed. In addition,
using geospatial, population, traffic, and travel-to-work information, it is possible to
locate the best place for a hospital, school, or community facility, to serve commu-
nities most at need, or cut travel times. Moreover, Ministry of Social Development
is using data to better learn which of its services get better outcomes for individuals
and communities in order to waste less public expenditure on services that do not
work and invest more on what does work (New Zealand Data Futures Forum).
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7 A Comparative Analysis of Tools and Technologies for Policy Making 147
7.3.11 Semantics and Linked Data Tools
Semantic technology enables users to enrich their documents and contents with
machine-processable semantics of data that make use of metadata to enable more
sophisticated data mining (Berners-Lee et al. 1999). The explicit representation of
the semantics of data is accompanied by domain theories, namely ontologies. Linked
data is based on semantic web philosophy and technologies, but, in contrast to the full-
fledged semantic web vision, it is mainly about publishing structured data in Resource
Description Framework (RDF) using uniform resource identifiers (URIs) rather than
focusing on the ontological level or inferencing (Hausenblas 2009). Thus, linked
data refer to the ability to link together different pieces of information published
on the Web and the ability to directly reference to a specific piece of information
(Cyganiak et al. 2011; Heath and Bizer 2011).
Responding to this trend, traditional content management systems (CMS) have
been improved to support semantic technology and provide semantic lifting of the
textual content (Auffret 2001). For example, new CMS enable users to (collaborative)
elaborate their documents and online texts submitting comments and annotations
(e.g. Enrycher, Annotea). In other cases, users can define and store data based on
custom ontologies created by them (e.g. WebNotes). Furthermore, some CMS have
tried to support linked data techniques such as automatic detection of entities such as
persons, places, and locations, and their linking to external sources, e.g. to dbpedia
descriptions of resources (e.g. Apache Stanbol). On the other hand, several tools have
been created to address collaborative creation of ontologies (OntoMat-Annotizer,
OntoGen).
Considering the recent shift towards massively offering open nonpersonal gov-
ernment data, one can easily understand the importance of linked data in the field
of policy making (Kalampokis et al. 2011). One example of how Linked Open Data
may be effectively used to inform discussions held by policy makers and others is the
clean energy information portal, Reegle27. This portal interprets raw data in order to
provide useful information and context for end users: It provides high-quality infor-
mation on renewable energy efficiency and climate compatible development around
the world as easily navigable graphs and tables with a lot of additional information
at hand too.
7.4 Summary and Discussion
In the previous section, we presented emerging tools and technologies with the
potential to enhance policy making. In this section, we would like to provide an
overall discussion of this potential, especially with regard to three main aspects for
policy making:
27 http://www.w3.org/2012/06/pmod/report.
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148 E. Kamateri et al.
• The main activities facilitated by each tool and technology. Previous analysis
showed that each tool category presents a different way for enhancing policy mak-
ing. For example, some tools focus on providing information in a user-friendly
manner, other tools promote deliberation, other tools are used to gauge pub-
lic opinion, etc. Analysing this characteristic, we can draw conclusions on the
different ways each emerging tool and technology may be used in policy making.
• The stage of the policy cycle facilitated by each tool and technology. It was
previously mentioned that the policy making process is composed of a number of
stages; these stages describe the policy life cycle. Analysing the “fit” of each tool
and technology in the policy cycle stages promotes understanding of how each
tool and technology can enhance the policy-making process. We will consider
four main stages of the policy cycle as they were defined by Jann and Wegrich
(2006): Agenda setting; policy formulation and decision making; implementation;
evaluation and termination.
• The stakeholder types that can use each tool and technology. We categorise the
previously identified stakeholders in policy making as follows: institutional stake-
holders (i.e. the government), noninstitutional stakeholders (i.e. political parties,
political consultants, and lobbyists, the media, nongovernmental organisations,
civil organisations, and other interested parties), and the public. Analysing who
of these stakeholder groups could use each tool and technology and in what ways,
promotes understanding of how these tools and technologies can be adopted in
policy making.
Following this, we examined each category of the identified tools and technologies
with regard to these three aspects.
Visualisation tools are ideal mainly for information provision, namely for present-
ing data in a user-friendly, easy-to-grasp representation. These tools can be used in
any stage of the policy cycle, wherever the need for demographic, social, or spatial
data representation emerges. For example, they can be used during the decision-
making stage in order to fine-tune new policies, during the implementation and
evaluation stage in order to understand whether the application of a certain policy
brought any changes or even during the agenda-setting stage in order to identify
problems that should be addressed with policies. All types of stakeholders may be
potential users of visualisation tools depending on the topic addressed and due to the
fact that no specialisation is required in order to use and understand them.
Argumentation tools are ideal for structured deliberation, namely for discussing
specific issues with the aim to reach a common understanding or a commonly ac-
cepted decision. As such, these tools can be useful in all stages of the policy cycle,
whenever a targeted deliberation is needed; maybe they are more relevant for the
agenda setting, the policy formulation and decision making, and the evaluation and
termination stages where such discussions are usually performed. With regard to
potential users, in principle, all stakeholders can use argumentation tools. However,
previous experience in the field has shown that argumentation tools require a cer-
tain degree of logic and critical thinking. It is, therefore, not easy for the general
public to productively use these tools without prior training (Tambouris et al. 2011
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7 A Comparative Analysis of Tools and Technologies for Policy Making 149
and Panopoulou et al. 2012). For this reason, argumentation tools may be more ef-
fectively used for somewhat “closed” deliberation groups targeting a specific issue
within a certain policy field.
eParticipation tools are ideal for involving the public in the policy-making process.
They refer to many different activities such as information provision, deliberation,
consultation, gauging public opinion, citizen engagement, community building, etc.
eParticipation tools may be initiated by an institutional stakeholder (top-down partici-
pation) or a noninstitutional stakeholder or even the public (bottom-up participation).
Thus, all stakeholder types are potential users of these tools, although typical usage
refers to interactions between the government and the public. Due to the wide spec-
trum of supported activities, eParticipation tools may be used in any stage of the
policy cycle.
Opinion mining tools are ideal for gauging the public’s opinions and sentiments,
thus, they can be used in any stage of the policy cycle whenever such a service is
needed. For example, they can be used for gauging the acceptance potential of a
new policy or for detecting negative evaluations of a policy. Due to their technical
complexity, opinion mining tools are better suited to be used by trained institutional
stakeholders or noninstitutional stakeholders, but not the general public.
Simulation tools are useful in policy making for detecting and simulating social
interactions and behaviour patterns. For example, they can be used for simulating
the long-term impact of different policy alternatives and thus assist in the policy
formulation and decision-making stage. Simulation tools are technically complex
to implement; therefore, they are mostly suited for usage by a few specialised
institutional or noninstitutional stakeholders.
Serious games are useful in policy making for educational purposes. They are
mostly relevant to the policy formulation and decision-making stage of the policy
cycle, as players may assume a stakeholder’s role in order to explore different policy
scenarios on a given topic and make relevant decisions. Serious games can also
be used in the implementation stage of the policy cycle, for educating citizens on
how to apply a certain state policy, e.g. a health or environmental policy. The main
stakeholder group of serious games is the wide public.
The two tools included in our analysis that were specifically developed for policy
makers are relevant to the policy formulation and decision-making stage and to the
evaluation and termination stage of the policy cycle. Of course, their user group
includes only institutional or noninstitutional stakeholders.
Persuasive tools can be used by institutional or noninstitutional stakeholders
for influencing public attitudes and behaviours. Thus, it is mostly relevant to the
implementation stage of the policy cycle, for strengthening policy adoption.
Social network analysis tools are useful for identifying key actors and social
patterns relevant to specific policy areas. These can be used in the policy formulation
and decision-making stage, and in the implementation stage of the policy cycle for
deciding alternative policies or for strengthening policies’ implementation. SNA is a
complex process requiring specialised knowledge, thus it can only be used by trained
institutional or non-institutional stakeholders.
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150 E. Kamateri et al.
Big data analytics tools can be useful in policy making for processing huge
amounts of information and, through this, for detecting and predicting patterns and
trends of the public. These activities are relevant to all stages of the policy cycle,
maybe less relevant to the implementation stage. Nonetheless, the users of this tech-
nology can be the government per se or noninstitutional stakeholders interested in
analysing data for a specific topic.
Semantics and linked data tools can be exploited for enhancing interoperability of
government data and for creating linkages between open government data and social
data. Thus, linked data tools can facilitate better understanding of social data and
public opinion and better prediction of public reactions, e.g. to different policy alter-
natives. For this reason, semantics and linked data tools seem relevant to all stages
of the policy cycle. Again, the specialty required for applying these technologies
means that only institutional or noninstitutional stakeholders may be the immediate
users of such technologies (Table 7.8).
The table above shows that a policy stakeholder has a number of different ICT
tools and technologies at hand. From these, they could choose the most appropriate
ICT mix depending on the targeted activity and policy-making stage. For example,
we can draw the following conclusions:
• Visualisation tools, argumentation tools, opinion mining tools, big data, linked
data, and eParticipation tools may be used at any point of the policy-making
process depending on the activities needed.
• Serious games and persuasive tools are the most appropriate in order to strengthen
the implementation stage and promote policy adoption.
• The policy formulation and decision-making stage of the policy cycle is the
most frequently addressed stage. This is not surprising as this stage involves
multiple and diverse activities such as scenario analysis, policy drafting, public
consultations, and decision making.
• Visualisation tools, big data analytics tools, and linked data tools can help enhance
provision and analysis of large amounts of information.
• A number of different technologies have emerged for detecting opinions, senti-
ments, trends, and other patterns of behaviour: opinion mining, simulation, social
network analysis, big data analytics tools, and linked data tools. There is clearly a
trend for using modern ICT towards analysing crowd knowledge already available
online.
• For exploiting advanced tools and technologies expert skills are needed that can
only be hired in the context of big (institutional or noninstitutional) organisations.
• For involving the public, visualisation tools, eParticipation tools, and serious
games are the most appropriate choices.
Acknowledgments This work is partially funded by the European Commission within the
7th Framework Programme in the context of the eGovPoliNet project (http://www.policy-
community.eu/) under grand agreement No. 288136.
w.jager@rug.nl

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7 A Comparative Analysis of Tools and Technologies for Policy Making 151
Table 7.8 Potential of emerging tools and technologies for enhancing policy making
Tools and
technologies
Main activities Policy cycle stages Stakeholder types
Visualisation
tools
Information
provision
All All
Argumentation
tools
Structured
deliberation
All (possibly less in
the implementation
stage)
All (though not easy
for untrained public)
eParticipation
tools
Information
provision,
deliberation,
gauging opinions,
citizen engagement
All All, typically for
interaction between
the public and the
government
Opinion-mining
tools
Gauging opinions
and sentiments
All Institutional or
noninstitutional
stakeholders
Simulation tools Detecting and
simulating social
interactions and
behaviour patterns
Policy formulation
and decision
making
Institutional or
noninstitutional
stakeholders
Serious games Policy education Policy formulation
and decision
making,
implementation
The public
Tools specifically
developed for
policy makers
Policy analysis and
assessment
Policy formulation
and decision
making, evaluation
and termination
Institutional or
noninstitutional
stakeholders
Persuasive tools Influencing public
attitudes and
behaviours
Mostly relevant to
Implementation
Institutional or
noninstitutional
stakeholders
Social network
analysis tools
Identifying key
actors and social
patterns
Policy formulation
and decision
making,
implementation
Institutional or
noninstitutional
stakeholders
Big data
analytics tools
Information
processing,
detecting and
predicting patterns
and trends
All (possibly less in
the implementation
stage)
Institutional or
noninstitutional
stakeholders
Semantics and
linked data tools
Understand
opinions, predict
public reaction
All Institutional or
noninstitutional
stakeholders
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152 E. Kamateri et al.
Appendix
Visualisation Tools
China Geo – Explorer II http://chinadataonline.org/cge
Data Visualizer-World Bank http://devdata.worldbank.org/DataVisualizer
DataPlace http://www.dataplace.org http://devdata.worldbank.org/DataVisualizer
Dynamic Choropleth Maps http://www.turboperl.com/dcmaps.html
e-Atlas of Global Development–World Bank http://data.worldbank.org/products/
data-visualization-tools/eatlas
Gapminder http://www.gapminder.org/tag/trendalyzer
Google Charts https://developers.google.com/chart
Google Public Data Explorer http://www.google.com/publicdata/directory
Health Infoscape http://senseable.mit.edu/healthinfoscape
NComVA http://www.ncomva.com
OECD eXplorer http://stats.oecd.org/OECDregionalstatistics
PolicyMap http://www.policymap.com
R http://www.r-project.org
Social Explorer http://www.socialexplorer.com
STATcompiler http://www.statcompiler.com
State Cancer Profiles http://statecancerprofiles.cancer.gov/micromaps
Visokio Omniscpoe http://www.visokio.com
Visualize Free http://visualizefree.com
Worldmapper http://www.worldmapper.org
Argumentation Tools
Araucaria http://araucaria.computing.dundee.ac.uk/doku.php
Argunet http://www.argunet.org
bCisive https://www.bcisiveonline.com
Carneades http://carneades.github.io
Cohere http://cohere.open.ac.uk
Compendium http://compendium.open.ac.uk/institute
Cope_it! http://copeit.cti.gr/Login/Default.aspx
DebateGraph http://debategraph.org
MindMeister http://www.mindmeister.com
Rationale http://rationale.austhink.com
eParticipation Tools
Citizen Space https://www.citizenspace.com/info
Adhocracy.de http://code.adhocracy.de/en
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7 A Comparative Analysis of Tools and Technologies for Policy Making 153
CitySourced https://www.citysourced.com
CivicEvolution.org http://civicevolution.org
Dialogue App http://www.dialogue-app.com/info/
Loomio.org https://www.loomio.org/
MixedInk.com http://www.mixedink.com
Opinion Space http://opinion.berkeley.edu
OurSpace http://www.ep-ourspace.eu/
Puzzledbypolicy http://www.puzzledbypolicy.eu
UbiPol http://www.ubipol.eu/
Opinion Mining Tools
AIRC Sentiment Analyzer http://airc-sentiment.org
Attentio http://www.attentio.com
Convey API https://developer.conveyapi.com
Corpora’s Applied Linguistics http://www.corporasoftware.com/products/
sentiment.aspx
DiscoverText http://www.discovertext.com
Opinion observer http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.79.
8899
Opinmind http://www.opinmind.com
Repustate https://www.repustate.com
Sentimentor http://sentimentor.co.uk
Sentiment140 http://www.sentiment140.com
Social Mention http://socialmention.com
SwiftRiver http://www.ushahidi.com/products/swiftriver-platform
ThinkUp https://www.thinkup.com/
Umigon http://www.umigon.com/
Agent-Based Modelling and Simulation Tools
Budget simulator http://www.budgetsimulator.com/info
C-ROADS http://climateinteractive.org/simulations/C-ROADS
CLASP’s Policy Analysis Modeling System (PAMS) http://www.clasponline.org/
en/Tools/Tools/PolicyAnalysisModelingSystem
EUREAPA tool https://www.eureapa.net/
GLEaMviz http://www.gleamviz.org/simulator
Global Buildings Performance Network (GBPN) Policy Comparative Tool
http://www.gbpn.org/databases-tools/purpose-policy-comparative-tool
MASS http://mass.aitia.ai
MEL-C http://code.google.com/p/jamsim
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http://www.corporasoftware.com/products/sentiment.aspx

http://www.corporasoftware.com/products/sentiment.aspx

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.79.8899

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.79.8899

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154 E. Kamateri et al.
Repast http://repast.sourceforge.net
Threshold 21 http://www.millennium-institute.org/integrated_planning/tools/T21
UrbanSim http://www.urbansim.org/Main/WebHome
Serious Games
2050 Pathways https://www.gov.uk/2050-pathways-analysis
Breakaway (Disaster Management-Incident Commander) http://www.
breakawayltd.com
Budget Hero http://www.marketplace.org/topics/economy/budget-hero
CItyOne http://www-01.ibm.com/software/solutions/soa/innov8/cityone/index.
html
Democracy http://www.positech.co.uk/democracy
Maryland Budget Map Game http://iat.ubalt.edu/MDBudgetGame
MP For A Week http://www.parliament.uk/education/teaching-resources-lesson-
plans/mp-for-a-week-game/
NationStates—create your own country http://www.nationstates.net
The Social Simulator http://www.socialsimulator.com
Urgent Evoke http://www.urgentevoke.com
World Without Oil http://worldwithoutoil.org
Policy-Making Tools
Oracle Policy Automation for Social Services http://www.oracle.com/us/industries/
public-sector/059171.html
PolicyMaker http://www.polimap.com/default.html
Semantics and Linked Data Tools
Annotea http://www.w3.org/2001/Annotea
Apache Stanbol http://stanbol.apache.org
Enrycher http://ailab.ijs.si/tools/enrycher
OntoGen http://ontogen.ijs.si
OntoMat-Annotizer http://annotation.semanticweb.org/ontomat
Reegle http://www.reegle.info
WebNotes http://www.webnotes.net
w.jager@rug.nl

http://www.millennium-institute.org/integrated_planning/tools/T21

http://www.breakawayltd.com

http://www.breakawayltd.com

http://www-01.ibm.com/software/solutions/soa/innov8/cityone/index.html

http://www-01.ibm.com/software/solutions/soa/innov8/cityone/index.html

http://www.oracle.com/us/industries/public-sector/059171.html

http://www.oracle.com/us/industries/public-sector/059171.html

7 A Comparative Analysis of Tools and Technologies for Policy Making 155
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Chapter 8
Value Sensitive Design of Complex Product
Systems
Andreas Ligtvoet, Geerten van de Kaa, Theo Fens, Cees van Beers,
Paulier Herder and Jeroen van den Hoven
Abstract We increasingly understand technical artefacts as components of complex
product systems. These systems are designed, built, maintained, and deprecated by
stakeholders with different interests. To maintain interoperability between compo-
nents, standards are being developed. The standardisation process itself is, however,
also influenced by different stakeholders.
In this chapter, we argue that a full, comprehensive overview of all relevant
components of a system is increasingly difficult. The natural response to complex
problems is to delve into details. We suggest that an opposite move towards a more
abstract approach can be fruitful. We illustrate this by describing the development of
smart meters in the Netherlands. A more explicit focus on the values that play a role for
different stakeholders may avoid fruitless detours in the development of technologies.
Policymakers would do well by not only addressing functional requirements but also
taking individual and social values into consideration.
8.1 Complex Technology
Modern society is highly dependent on a number of infrastructures; electricity and
telecommunications infrastructures are considered most critical (Luiijf and Klaver
2006). The desire to move towards a more sustainable energy system with a more
decentralised structure, and with a focus on renewable energy sources such as solar
energy and wind power, requires adjusting the existing, centralised electricity in-
frastructure. By adding information and communication technologies (ICT) to the
electricity grid at all levels of the system—from high-voltage transformers to washing
machines—each node in the network can decentrally respond to its neighbourhood
while safeguarding the reliability of the whole system. The concept of such a new
electricity infrastructure is known as the smart grid.
The smart grid concept implies a number of changes at various system levels
(national transmission grid, local distribution grid, and residential connections;
A. Ligtvoet (�) · G. van de Kaa · T. Fens · C. van Beers · P. Herder · J. van den Hoven
Faculty of Technology, Policy, and Management, Delft University of Technology,
Delft, The Netherlands
e-mail: a.ligtvoet@tudelft.nl
© Springer International Publishing Switzerland 2015 157
M. Janssen et al. (eds.), Policy Practice and Digital Science,
Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_8
w.jager@rug.nl

158 A. Ligtvoet et al.
Morgan et al. 2009) with a large role for ICTs (Mulder et al. 2012). However,
the precise technological constellation of smart grid systems is yet unknown, and
a matter of discussion for politicians and policymakers, (systems) engineers and
standardisation bodies, energy providers and distributors, knowledge institutes and
telecommunication organisations, and citizen representatives.
Even when limiting ourselves to the residential realm the number of interrelated
issues is vast. A case in point is the smart meter. This improved version of an electricity
meter is seen as an important element for electricity grid optimisation that also allows
for end-user efficiency through insight into consumption patterns (EC 2011). The
most comprehensive version of such a device provides information about household
energy consumption (accounting for decentral generation). The smart meter transmits
this information to energy providers and/or distributors to improve their systems,
and control of electric devices remotely, for example, to optimise the load of the
distribution grid, or to switch off consumers who have not paid their bills. In practice,
however, smart meter deployment is guided by various motives (e.g. fraud detection
or improved billing) that have different technical requirements (AlAbdulkarim 2013).
At the same time, it has become clear that the roll-out of smart meters can only be
successful if the end users in households also recognise their benefits (Cuijpers and
Koops 2013; Hierzinger et al. 2013). Until recently, this has not been the case
and citizens have voiced concerns about issues including privacy (McDaniel and
McLaughlin 2009) and health effects (Verbong et al. 2013; Hess and Coley 2012).
In this chapter, we take the position that technology development is driven by the
needs and requirements of a wide range of stakeholders. Among these, technology
developers and their competitors play an important role in shaping and standardising
technologies. Other stakeholders, such as households, may have a less prominent
role in determining the development of technologies, but at times play a significant
role in the acceptance of the technology (Mitchell et al. 1997). It is important to
identify all the stakeholders involved, and to understand their motives and values
so that the technology development can be adjusted in a timely fashion. The Dutch
smart metering history provides a cautionary tale as the needs of households end
users, one of the main stakeholders, were not sufficiently taken into account. This
was one of the main reasons that the Dutch Senate rejected the proposed Energy Bill
in 2008 (Cuijpers and Koops 2013) which consequently delayed the roll-out of smart
meters for several years.
We aim to shed light on the development process of the complex product system
and examine to what extent value-sensitive design (VSD) could have avoided this
delay. We employ a case study analysis of the standardisation1 of smart metering in the
Netherlands. We add insights from overlapping standards discussions in household
automation and show that the interlinked nature of ICT, consumer products, energy
systems, and home automation does not allow a strict delineation of technological
1 We follow the definition of standardisation as proposed by de Vries (1999): An activity of estab-
lishing and recording a limited set of solutions to actual or potential matching problems, directed
at benefits for the party of parties involved, balancing their needs and intending and expecting that
these solutions will be repeatedly or continuously used, during a certain period, by a substantial
number of the parties for whom they are meant.
w.jager@rug.nl

8 Value Sensitive Design of Complex Product Systems 159
2004 2007 2010 2013
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Fig. 8.1 Timeline of the smart meter policy process in the Netherlands
artefacts, and leads to an oversimplification of the issues at stake. We discuss to what
extent earlier analysis of this information could have led to adjustments of standards
such as the Dutch smart metering requirements (DSMR).
8.2 Smart Meters in the Netherlands
Early scholarly mentions of intelligent or smart meters suggest their (technical)
development took place in the 1980s and 1990s (see e.g. Peddie 1988). As we
are interested in official standardisation, we provide an overview of Dutch policies
regarding smart meters (see also Fig. 8.1), its standardisation, and the stakeholders
involved in this process.
Following a letter about security of energy supply from the Ministry of Economic
Affairs to Dutch Parliament in 2003 (MinEZ 2003), SenterNovem, a ministry agency,
was requested to investigate the standardisation, stakeholder involvement, and con-
duct a cost–benefit analysis on the roll-out of a smart meter infrastructure (Dijkstra
et al. 2005). Demand-side response was seen as a major contribution to security
of supply during peak electricity consumption. The Dutch standardisation institute
Nederlands Normalisatie-Instituut (NEN) was commissioned to formulate and de-
scribe a national standard for smart meters. The societal cost–benefit analysis proved
to be positive (a net gain of 1.2 billion €) with the citizens as main beneficiaries of the
roll-out. Interestingly, in the ensuing stakeholder consultation, consumer represen-
tatives were not heavily involved: “The point of view of the consumer, individually
as a household, or collective via housing corporations, Home Owners Association
or Consumers Association was not a key issue” (Dijkstra et al. 2005). The other
stakeholders—energy producers, energy suppliers, grid operators, metering compa-
nies, telecom, energy regulators—requested the ministry to clearly identify meter
functionalities, expedite meter roll-out by setting a time frame, and provide regular
consumption overviews (to make smart meters the only affordable solution).
Anticipating the EU Directive 2006/32/EC on on energy end use and energy
services, the Ministry of Economic Affairs provided more information on smart
meters requirements, citing billing administrative problems and the energy savings
w.jager@rug.nl

160 A. Ligtvoet et al.
goals of the Commission as main arguments in favour of smart meters (MinEZ
2006). In 2007, NEN published the technical agreement NTA 8130, which set out a
minimum set of requirements for smart metering. The organisation of grid operators
(Netbeheer Nederland) took the lead in specifying these requirements, which became
DSMR.
In 2008, the Ministry of Economic Affairs revised the electricity and gas bills that
implemented the European directive. Grid operators became responsible for meter
deployment, and energy providers were appointed the point of contact for consumers.
This was supposed to increase clarity for consumers, efficiency, and create a level
playing field for market parties. Consumers were required to cooperate in installing
smart meters; not doing so would constitute an economic felony. After several rounds
of reviews and discussions about privacy, and amendments as a response to the Dutch
Data Protection Authority (Customs and Border Protection, CBP), the bills were
passed by the Lower House of Parliament in July 2008. By that time, the smart meter
and its privacy issues had gained wider public interest. Technical experts assessed
possible security and privacy breaches of the meter, and legal experts deemed the
proposed solution irreconcilable with the European Convention on Human Rights
(Cuijpers and Koops 2013). When the bills were scrutinised by the Senate in 2009,
it proposed amendments regarding the mandatory character of smart meters and
revisions concerning consumer privacy.
The smart metering bill was amended into a voluntary roll-out of smart meters and
reintroduced for political consideration in September 2010. The customer could now
decline a smart meter and energy suppliers were required to give customers bimonthly
statements with specific minimum information requirements. The grid operators set
up uniform authorisation and authentication procedures to ensure that individual
measurement data were only used for specific purposes and only after customer
consent. The revised bills passed the Lower House of Parliament in November 2010
and was approved by the Senate in February 2011 (Hierzinger et al. 2013).
The Ministry of Economic Affairs agreed on a “small-scale” deployment of smart
meters in 2012 and 2013. This 2-year period was used to test the practical implications
of roll-out in approximately 400,000 households and to assess consumer response. A
midterm review of the roll-out did not identify any major issues, with only 2–3 % of
households rejecting the smart meter. At the end of 2013, there is still a political debate
about whether the smart meter should be coupled with the functionality to switch off
electricity and gas. In other countries, this was the main reason to install smart meters,
but the Dutch Consumers’Association argued that remote-controlled switches would
constitute a cyber threat on a nationwide scale. In its latest consultation round, the
ministry seems to share this view.
Meanwhile, several stakeholders (notably hardware providers) argue that the
Netherlands with its 8 million households and 750,000 small and medium enter-
prise connections is not large enough to make a customised smart meter financially
feasible. They emphasise that the DSMR should be abandoned in favour of European
standards.
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8 Value Sensitive Design of Complex Product Systems 161
8.3 Smart Meters as Complex Product Systems
A smart meter could be seen as an artefact that, like a pair of scissors, can be de-
signed or bought on the market in relative isolation. However, our case study already
indicates that the development of smart meters is contingent upon developments
on international, national, and (inter)organisational levels. Literature on technology
management has long conceptualised technological artefacts as subsystems that are
linked together, as well as being a component of even larger systems (Clark 1985;
Suarez 2004). Tidd (1995) calls these complex product systems, that have three
distinctive characteristics:
• Systemic: the systems consist of numerous components and subsystems.
• Multiple interactions take place across different components, subsystems, and
levels.
• Nondecomposable: the systems cannot be separated into their components without
degrading performance.
This means that technologies, components, and interfaces incorporated in products
are interdependent, and thus rely on standard interfaces, but also depend on differ-
ent market segments and the range and specificity of performance criteria within
these markets. This also means that technological designs, sponsored by different
actors, compete for dominance through a process where economic, technological,
and sociopolitical factors are intertwined (Rosenkopf and Tushman 1998). The more
complex the product system, the greater is the number of actors needing to be aligned
for a technological design, and thus the more complicated the actual design process
becomes (Suarez 2004).
In the following sections, we indicate that the development of smart meters and
home energy management systems (HEMS) is influenced by competing formal and
industry standards (Sect. 8.3.1) and that a whole range of actors or stakeholders is
involved in the development of these artefacts (Sect. 8.3.2). By combining these two
analyses, a multifacted picture emerges.
8.3.1 Competing Standards
In the decision about smart meters and HEMS, competing formal and nonformal
standards play a role. We have attempted to provide an overview of different standards
that are related to these technologies in Table 8.1. This overview shows us that there
are many options for the design of smart meters/HEMS components. Whereas, there
may be some room for consolidation, some of the presented standards provide unique
solutions to specific problems. As Gallagher (2007) indicates, it remains extremely
difficult to pick “winners” ex ante.
Whereas smart metering falls under governmental regulation, the market for
HEMS is not regulated. However, depending on the final specifications of the
smart meter, some functionalities may overlap. Many different established and
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162 A. Ligtvoet et al.
Table 8.1 Overview of different (competing) standards in the converging technology realms
Component/Subcomponent Competing standards
Smart meter
Smart meter NTA 8130, DRMS X.X, DLMS, IEC 62056-21, NEN-EN 13757,
IEEE Std 1901, IEEE P1703, IEEE 1377, DLMS/COSEM
standard (IEC 62056 / EN 13757-1), IEEE 802.15.4, Wired
M-Bus, M-Bus protocol (EN 13757), 6LoWPAN, ANSI C12.18,
IEC 61107
Communication systems
Wired local area networks
(application level)
Arcnet vs ATM vs CEPCA vs Ethernet vs FDDI vs Home plug
and play vs homeplug vs hiperlan2 vs open air vs Passport vs
Powerpacket, Smart Energy Profile 2, Universal Powerline Bus
(UPB), DMX512
Wired local area networks
(infrastructure level)
FRF vs MPLS/Framerelay vs Orthogonal frequency divising
multiplexing vs Salutation vs SSERQ vs Token Bus vs Token
Ring vs UPA
Wireless local area
networks (infrastructure
level)
HomeRF vs IEEE802.16 vs Open air vs IEEE802.11(Wifi),
HiperLAN
Wireless personal area
network
Bluetooth vs IEEE 802.15.3 vs IEEE 802.15.4 vs Irda vs Zigbee,
Z-wave, 6LoWPAN
Power line communication IEEE Std 1901-2010, HomePlug,G.hn(G.9960), PRIME,
PLC-G3, IEC-61334 SFSK
Computer networks
(wired)
USB vs Convergence bus vs Firewire vs IRDA
Mobile
telecommunications
3G vs Dect vs GPRS vs GSM vs UMTS
Home automation
systems
Home networks DLNA vs HANA vs HAVi vs HomeAPI vs HOMAPNA vs Moca
vs UPnP, IEC/TS 62654, NEN-ISO/IEC 15045-1, ISO/IEC
14543-3-7, IEC 61970, IEEE 1905.1, ITU-T G.9960,
CEA-2027-B, CEA-2033, CAN/CSA-ISO/IE, NEN-EN 50090-1,
MultiSpeak, IEC 62457, H950 SystemLink
Home automation (wired
and wireless)
CEA851 vs CEBus vs Echonet vs EHS vs HBS vs HES vs HGI vs
HomeCNA vs HomeGate vs HPnP vs Lontalk vs Smarthouse,
ISO/IEC 14543KNX, Zigbee, digitalSTROM, ISO/IEC TR
15044, EN 50090 (KNX/EIB)
Building automation BACnet vs BatiBUS vs COBA vs DALI/IEC 60929 vs FND vs
Instabus vs KNX vs Metasys vs MOCA vs Profibus vs Worldfip
vs X10 vs Zigbee, NPR-CLC/TR 50491-6-3, ISO
16484-5BACnet NEN-EN 13321-1, NEN-EN 15232, ISO
16484-5, ISO 50001, ISO/IEC 18012-1, EnOcean, Modbus, oBIX
Consumer electronics
Video Displayport vs DVI vs HDMI vs Scart vs VESA vs VGA
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8 Value Sensitive Design of Complex Product Systems 163
Table 8.1 (continued)
Component/Subcomponent Competing standards
Energy systems
Heat/cold storage NEN-EN-IEC 60531, NEN-EN-IEC 60379
Electric stationary storage
batteries
J537, 1679-2010 IEEE
Electric car batteries SAE J2847, SAE J2836/1-3, SAE J2931/5, SAE J2758, Smart
Energy Profile (SEP 1.1), ASTM D 445, DIN 51 562 (part 1) ,
ISO 3105
Decentral electricity
production systems
Solar photovoltaics IEC 61215, IEC 61646, UL 1703, IEC 60904
Small wind mills DIN EN 61400-25-4, AGMA 6006-A03, NEN 6096
Micro CHPs DIN 4709
newly emerging industries and product markets are involved in HEMS (den Har-
tog et al. 2004). We observe a convergence between established industries such as
telecommunications, consumer electronics, and home automation (domotics) with
new developments in energy industries: photovoltaics, micro heat and power, mi-
cro wind, storage, and home automation. ICT plays a crucial role at both national
and local level. In the energy sector, integrating information technology with oper-
ational technology (IT/operations technology (OT) integration) is seen as a major
development. In short, ICT is what makes a smart grid smart.
Actors that originate from the different converging industries develop and pro-
mote standards that enable communication not only for components within single
industries but also for communication between components that originate in differ-
ent industries (van de Kaa et al. 2009). van de Kaa et al. (2009) have performed a
search for standards for home networking and present a graphical overview of these
standards that originate in different converging industries. We use that graphical
overview and have adapted it for the situation of HEMS (see Fig. 8.2).
While some of the standards mentioned in Fig. 8.2 clearly belong in one industry,
we also see shifts taking place. We have indicated two of these shifts in the figure.
Whereas Universal Serial Bus (USB) started off in the computer industry, it became
increasingly used in consumer electronics, e.g. for allowing MP3 music files to be
played on audio systems. Likewise, Digital Enhanced Cordless Telecommunications
(DECT) was orginally a wireless telephony protocol, but was soon used in baby
monitors, and since the development of the Ultra Low Energy variant in 2011 it is
used in home appliances, security, healthcare, and energy monitoring applications
that are battery powered.
w.jager@rug.nl

164 A. Ligtvoet et al.
USB
DECT
SCART
COAX
USB
HDMI
Firewire
WiFi
KNX
ZigBee
Zwave
PLC
M-bus
DECT
Information and communication technology
Consumer
Electronics
Energy
Home automation
UPnP
EchoNet
EHS
HES
COBA
Lonworks
Batibus
EIB
BACnet
X10
Metasys
DALI
PLC
IEC60531
IEC60379
IEC60904
DIN 4709
NEN 6096
TCP/IP
GSM
GPRS
UMTS
IEEE1901/1905
SEP2.0
ISO 9241-11
EN 50491-12
Fig. 8.2 Converging formal and industrial standards in the realm of home energy management
8.3.2 Actor or Stakeholder Analysis
The fact that industries are converging broadens the number of stakeholders involved
in technical developments. Although many of the traditional players in the energy
field still play a role, opportunities have been created for niche players to take on a
larger role. de Vries et al. (2003) have identified search directions for stakeholder
identification: production chain, physical systems and their designers, end users
and related organisations, inspection agencies, regulators, research and consultancy,
education, representative organizations, and organised groups of stakeholders. We
take this categorisation as a starting point, but add standardisation bodies as an
additional category. We have attempted to capture the interaction of electricity grid
components within a value chain representation in Fig. 8.3.
1. We take the electricity production chain as a starting point. Important players are
the distribution system operators (DSOs). In the Netherlands, these include the
largest DSOs Alliander, Delta, Enexis, and Stedin. Other players are the energy
production companies (e.g. Nuon and RWE/Essent), energy suppliers (which
may be middlemen between production and consumption), and the national grid
operator Tennet.
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8 Value Sensitive Design of Complex Product Systems 165
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166 A. Ligtvoet et al.
2. Various technology providers are involved in the development and design of phys-
ical components that are part of the entire value chain. This includes companies
such as Cisco Systems, IBM, Philips, Honeywell, and Siemens, meter companies
such as Landis + Gyr, Echelon, Itron, and Iskra, and data managers and integra-
tors such as Ferranti. Whereas some companies specifically focus on one aspect
of the value chain, most have a broader involvement.
3. End users and related organisations are the actual home owners and tenants that
may be organised in local groups such as local home owners’ associations (e.g.
owners of houses in the same building or street) or national groups such as Verenig-
ing Eigen Huis (home owners association of the Netherlands). Expert consultation
revealed that consumer representation in standardisation committees is rare in
the Netherlands but in some countries such as the UK and Japan consumers are
involved in standard development.
4. Important inspection agencies with regard to smart meters include not only Keur-
ing van Elektrotechnische Materialen te Arnhem (KEMA), who are charged with
organising Dutch meter inspections, but also NMi Certin and Verispect.
5. Regulators (Autoriteit Consument en Markt (ACM) Energiekamer) and policy-
makers (Ministry of Economic Affairs (Ministerie van Economische Zaken, EZ))
are involved heavily in the smart meter component. And also the European Union’s
policies have effect upon the Dutch smart meters and HEMS.
6. Universities, research institutes, and consultants play an important and major role
in standardization for HEMS in the Netherlands. Noteworthy in this context are
KEMA (who have provided several cost-benefit analyses) and Nederlandse Or-
ganisatie voor Toegepast Natuurwetenschappelijk Onderzoek (TNO; who have
advised the Ministry and parliament). The Netherlands’ national metrology in-
stitute Van Swinden Laboratory (VSL) takes a special role in this category, as
it is charged with certifying the (tools for) inspection agencies. Other important
stakeholders include IT consultancy firms.
7. The education category is less relevant for the identification of stakeholders for
HEMS. Although a lot of universities are actively engaged in research relating to
smart grids, active participation in standardisation is rare.
8. National representative organisations include consumer organisations such as the
Consumers’ Association (Consumentenbond). But also DSOs are represented by
Netbeheer Nederland and energy producers are represented by Energie Neder-
land. At the European level, smart meter providers are represented by European
Smart Metering Industry Group (ESMIG). For other networks, see below.
9. Standardisation bodies operate at different levels. Internationally, there are Inter-
national Organization for Standardization (ISO), International Electrotechnical
Commission (IEC), and International Telecommunication Union (ITU), at the
European level Comité Européen de Normalisation (CEN), Comité Européen
de Normalisation Électrotechnique (CENELEC), and European Telecommuni-
cations Standards Institute (ETSI), and at the national level NEN. See Sect. 8.3.3
below.
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8 Value Sensitive Design of Complex Product Systems 167
ISO IEC ITU-TInternational
CEN CENELEC ETSIEuropean
NENDutch
TelecommunicationElectrotechnicalBuilding, gas, water
Fig. 8.4 Standardisation organisations in different technology fields at (inter)national levels
8.3.3 Networks of Stakeholders
Not only are the stakeholders mentioned in the previous section active in influencing
policy but also are members of various standards organisations and consortia.
Noteworthy are international formal standardisation organisations ISO (gen-
eral standards; related to smart meters buildings, gas, and water), International
Electrotechnical Commission (IEC; electrotechnical standards), and International
Telecommunication Union-Telecom Sector (ITU-T; telecommunication standards).
These organisations also have standardisation organisation counterparts active on the
European level (CEN, CENELEC, and ETSI), and at the national level (in the Nether-
lands this is NEN; see Fig. 8.4). Most standardisation organisations have different
work groups or technical committees that develop standards for particular product
markets and/or technical areas. Smart meters, for example, are covered by IEC tech-
nical committee TC13 and its CENELEC counterpart (conveniently named TC13).
However, some aspects may be covered by other technical committees, such as TC57
on power systems management and associated information exchange. Members of
these work groups are to a large extent drawn from industrial partners or industrial
consortia. The members are, however, deemed to provide their expertise independent
of their employers.
Next to the formal standardisation organisations, various consortia exist that de-
velop and/or promote standards for components of HEMS. These standards may be
based on formal standards, but used in a specific application area. Several subcom-
ponents are combined to create a set of coherent components, some of which are not
formal standards. In the field of HEMS, these consortia include:
• KNX Association—promoting a standard for home and building control
• ZigBee Alliance—promoting a wireless technology designed to address the
unique needs of low-cost, low-power wireless sensor and control networks
• Salutation Consortium—promoting a service discovery and session management
protocol providing information exchange among and between different wireless
hand-held devices and office automation equipment.
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168 A. Ligtvoet et al.
• Echonet Consortium—promoting the development of basic software and hard-
ware for home networks that can be used for remote control or monitoring of
home appliances
• The Digital Living Network Alliance—publishing a common set of industry de-
sign guidelines that allow manufacturers to participate in a growing marketplace
of networked devices.
• Smart Grids European Technology Platform—a European forum for the crystalli-
sation of policy and technology research and development pathways for the smart
grids sector.
8.4 Values in the Design of Technical Artefacts
The vast amount of standards and stakeholders involved makes an overview of the
possible technological trajectories nearly impossible. We suggest that a focus on
values and the notion of VSD allows for a more comprehensive view of smart meter
and HEMS development.
Values are mentioned in a wide array of disciplines (e.g. philosophy, sociology,
economics) and generally denote what something is worth, opinions about that worth,
and/or moral principles (Dietz et al. 2005). Values are also described as “enduring
beliefs that a specific mode of conduct is personally or socially preferable to an
opposite or converse mode of conduct or end-state of existence” (Rokeach 1968).
In decision making, values, which can be described as an abstract set of principles,
allow us to resolve conflicts by suggesting which preferences are better. They provide
us with criteria to distinguish options (see, e.g. Keeney 1994).
Values also play a role in the design and use of technological artefacts. Whereas
historically technology may have been considered purely instrumental and value-free
(Manders-Huits 2011), it has become clear that technological artefacts exhibit moral
and political choices and consequences (even though the moral and political dimen-
sion may not be perceived by their designers and users). This means that the choice
for a specific technology may imply a social and institutional order without which
the technology might not work. Winner (1980) suggests that in “societies based on
large, complex technological systems, . . . moral reasons other than those of practical
necessity appear increasingly obsolete, ‘idealistic,’ and irrelevant. Whatever claims
one may wish to make on behalf of liberty, justice, or equality can be immediately
neutralized when confronted with arguments to the effect: ‘Fine, but that’s no way
to run a railroad’ (or steel mill, or airline, or communications system, and so on)”.
We would argue that the smart grid is one of those large, complex technological
systems, for which the same argument holds.
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8 Value Sensitive Design of Complex Product Systems 169
8.4.1 Value-Sensitive Design
A stream of research that focuses on moral and political dimensions of technology
and technology design is called Value Sensitive Design (VSD) (Friedman et al. 2002;
Borning and Muller 2012). VSD seeks to be proactive to influence the design of tech-
nology early in and throughout the design process. It employs conceptual, empirical,
and technical investigations (Friedman et al. 2008; van de Poel 2009):
• Conceptual investigations aim, for instance, at clarifying the values at stake, and
at making trade-offs between the various values.
• Empirical investigations involve social scientific research on the understanding,
contexts, and experiences of the people affected by technological designs.
• Technical investigations involve analysing current technical mechanisms and
designs to assess how well they support particular values, and, conversely, iden-
tifying values, and then identifying and/or developing technical mechanisms and
designs that can support those values.
Many of the technological examples addressed in VSD literature relate to ICTs (Fried-
man 1996; van den Hoven 2007; Friedman et al. 2008), which is why we expect the
approach to be pertinent to smart meter/home/grid technologies. VSD started from
the recognition that when designing information technologies, the predominant, tra-
ditional focus of engineers is on functionality, i.e. the efficiency, reliability, and
affordability of (new) technologies—conform the practical necessity argument iden-
tified by Winner (1980). Furthermore, the point of reference is often the designer’s
own experiences, needs, and values. For example, it has been shown that software
designers (unknowingly) design software that is more aligned with males than with
females. Friedman (1996) also mentions an example of educational software that is
geared towards the American competitive education system which is less successful
in foreign classrooms, where cooperation is considered more important.
8.4.2 Values in Our Research
Although it is embedded in moral philosophy, VSD uses a broad sense of values:
values refer to what persons, either singularly or collectively, consider important
to their lives. However, the 56 personal values that the Schwartz Value Survey,
commonly used in social sciences (Dietz et al. 2005), defines, might not relate to
technological artefacts and technology use. For this study, we therefore focus on a
subset that is often mentioned in VSD literature. Next to the already mentioned func-
tional values (accountability, correctness, efficiency, environmental sustainability,
legitimacy, reliability, safety, tractability), we address social values (cooperation,
courtesy, democracy, freedom from bias, identity, participation, privacy, trust) and
individual values (autonomy, calmness, economic development, informed consent,
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170 A. Ligtvoet et al.
ownership, universal usability, welfare). Most of these values are defined in Fried-
man et al. (2008). For the purposes of our research, we have translated these into
broad definitions which can be found in Table 8.2.
We have attempted to identify the values that played a role in the development
of smart meters (Ligtvoet et al. in press). Based on expert elicitation, the five most
important values associated with smart meters are:
1. Privacy: The system allows users to determine which information about them is
used and communicated.
2. Correctness: The system provides correct data or performs the correct function.
3. Reliability: The system fulfils its function without the need to monitor/control it.
4. Informed consent: The system allows its users to voluntarily agree to its activation,
based on comprehensible information.
5. Economic development: The system is beneficial to its users’ economic or
financial status.
These results very closely match the general impression of the smart metering debate
in the Netherlands. Privacy is a very important value that was virtually ignored at
the start of the implementation process. As could be expected for a device that is
designed to measure, the functional values of correctness and reliability are also
ranked high. The individual values of informed consent and economic development
emphasise that end users’ needs should be taken into consideration.
An interesting and unexpected finding of our expert group discussion was that
these values depend on the delineation of the system. The experts indicated that the
important values actually shift when the smart meter is not only seen as a connected
measuring device but also more as an energy management nexus for households.
This generates a new ranking of values for HEMS:
1. Economic development: The system is beneficial to its users’ economic or
financial status.
2. Universal usability: The system can easily be operated by all users.
3. Privacy: The system allows users to determine which information about them is
used and communicated.
4. Autonomy: The system allows its users to make their own choices and pursue
their own goals.
5. Reliability: The system fulfils its function without the need to monitor/control it.
Here, we see a clear shift towards the individual and social values of the users and
slightly less emphasis on the functional values of the technology. We believe that
this corresponds with findings of Krishnamurti et al. (2012) and Balta-Ozkan et al.
(2013). Compared with standalone smart meters, a clearly higher score was given for
participation and well being, again emphasising the user experience. Also, the values
legitimacy and freedom from bias became much less important. In the discussion,
it became clear that HEMS are seen as a commercial consumer product, for which
consumers are personally responsible.
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8 Value Sensitive Design of Complex Product Systems 171
Table 8.2 Overview of 23 values that we found in the value sensitive design literature
Value Description
Accountability The system allows for tracing the activities of individuals or
institutions
Autonomy The system allows for its users to make their own choices and
choose their own goals
Calmness The system promotes a peaceful and quiet state
Cooperation The systems allows for its users to work together with others
Correctness The systems processes the right information and performs the right
actions
Courtesy The system promotes treating people with politeness and
consideration
Democracy The system promotes the input of stakeholders
Economic development The system is beneficial to the economic status/finances of its users
Efficiency The system is effective given the inputs
Environmental
sustainability
The system does not burden ecosystems, so that the needs of current
generations do not hinder future generations
Freedom from bias The system does not promote a select group of users at the cost of
others
Identity The system allows its users to maintain their identity, shape it, or
change it if required
Informed consent The systems allows its users to voluntarily make choices, based on
arguments
Legitimacy The system is deployed on a legal basis or has broad support
Ownership The system facilitates ownership of an object or of information and
allows its owner to derive income from it
Participation The system promotes active participation of its users
Privacy The system allows people to determine which information about the
is used and communicateda
Reliability The system fulfils its purpose without the need to control or
maintain it
Safety and health The system does not harm people
Tractability The functioning of the system can be traced
Trust The system promotes trust in itself and in its users
Universal usability The system can be easily used by all (foreseen) users
Welfare The system promotes physical, psychological, and material
well-being
a We acknowledge that this is a limited definition of privacy
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172 A. Ligtvoet et al.
8.5 Discussion
8.5.1 From Values to Design Requirements
VSD purports to be a holistic approach that combines theory with empirics (Manders-
Huits 2011). The identification of values should therefore be linked to the formulation
of design requirements for complex product systems, in our case smart meters and
HEMS.
The need for (elements of) privacy was addressed in the NTA8130 standard and
the requirement of an encryption protocol was added. Functional requirements of
correctness and reliability were already covered by the Dutch measurement code and
no further requirements were necessary. Informed consent is not easily addressed
from a technological standpoint, but it did prove important in the debate in the Senate.
The solution was not technical, but procedural. The end user was given four options:
no smart meter but an ordinary one, a smart meter that does not communicate,
low-frequency communication, or high-frequency communication. The economic
development was addressed by several cost-benefit assessments and a restriction of
the metering tariff.
Given the nature of HEMS (i.e. more like a consumer product), the values as-
sociated with it should also be addressed in a slightly different way. The focus on
economic development suggests a restriction in the price of the system and a clear
indication of how much can be saved by installing such a system. Universal us-
ability emphasises the need for easy-to-use interfaces: end users should not require
an engineering degree to operate the system. Privacy remains an issue and requires
communication channels to be secured—similar to (mobile) telecommunication and
computing requirements. Autonomy suggests that the users should be in charge of
their home energy management and automation: this is closely linked to ease of use.
And finally, the system should be reliable like other consumer products.
We acknowledge that the current research has performed an ex post analysis of
values and identified issues that were already resolved in the course of history of the
Dutch smart meter (standard and requirements). The proof of the pudding would be
an ex ante assessment and monitoring of the upcoming issues.
8.5.2 Values Salience
Our research has led us to question the extent to which values are important and
identified, which one could call “values salience”. Comparing smart meters to other
technological systems such as communication systems or smart grids, we find that
the meter has received quite some attention. (On the basis of our interviews, we
believe that smart meters were initially only considered a technical issue.) We sug-
gest that values salience relates to the size of the technical system according to an
inverted U as shown in Fig. 8.5. This means that small technical components such
as communication protocols attract little attention and the general public remains
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8 Value Sensitive Design of Complex Product Systems 173
Fig. 8.5 Values salience of a
technical component depends
on its size
Technical component size
V
al
ue
s
sa
lie
nc
e
low
high
small large
System-
of-systems
(e.g. smart
grid)
System
(e.g. smart meter)
Technical
(sub)component
(e.g. RF protocol)
indifferent. The same argument holds for large systems such as an entire electricity
grid—although the public may still be able to judge some of the importance of such
an artefact for their own energy supply, they largely remain uninvolved. However,
the level of the household, thus of the smart meter, is most visible to people and
therefore their attention and ability to express values is much greater at this level.
8.5.3 Multidisciplinary Approach
Our research contributes to the literature on innovation management and standardis-
ation (e.g. Schilling 1998, 2002; Suarez 2004; Sheremata 2004). Scholars in the area
of innovation management and standardisation have attempted to explain standard
dominance and draw from various areas of research including network economics
and institutional economics (van de Kaa et al. 2011). They have come up with
technology-, firm-, and environmental-level factors that explain standard dominance
(Suarez 2004). In this chapter, we shed light on another level of analysis that is
neglected in the literature: the end user. We provide a first illustration of the notion
that societal acceptance of a platform will grow if a technological design is modified
to changing user requirements related to ethical and societal values surrounding the
technology. Privacy is the most salient value for the Dutch smart meter case, but
informed consent also played an important role. Combining literature from philoso-
phy and ethics on the one hand and technology management on the other hand, we
provide a clearer view on the influence of factors relating to the end user. Future
research could further explore the ex ante translation from identified values to actual
design requirements.
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174 A. Ligtvoet et al.
8.6 Conclusion
Our case study of the development of smart metering and smart metering standards in
the Netherlands shows the complexity of introducing new technologies in an existing
sociotechnical system. We believe that our findings can be generalised, not only to
other components of the smart grid but also to other systems-of-systems that are
deployed and used by a wide array of stakeholders.
Current technological development is often so complex that stakeholders are un-
able to fully assess new technologies. Nor are they able to weigh the input of all
stakeholders. First of all, because not all stakeholders are always involved and sec-
ondly, because people’s opinions and beliefs change because of new information,
insights, and experiences. Although the introduction of a new technical component
may start off from a very functional and technical position, nontechnical issues
(values) can be introduced by consumers’ associations and other stakeholders.
It can be argued that a lack of consideration for these values can lead to a delay
in the roll-out of new technologies. Even though technical solutions only seem to
address technical problems, they influence society through the interconnected nature
of modern infrastructures. Especially, when technology is “visible” at the household
level, consumers or their representatives can be expected to have an opinion. For pol-
icymakers, it would be wise to foresee such stakeholder involvement and to address
stakeholder values in an early stage.
The outcome of a values elicitation is a more balanced representation of the
interests of all stakeholders, including end users: a combination of functional, social,
and personal values. This focus on values may help designers in their search for
better technical and functional specifications. However, such a design process is
complicated by the fact that technical artefacts form an intricate part of larger systems-
of-systems. As we have shown, depending on the system focus, the related values are
somewhat different and there still may be some discussion to what extent an artefact
serves a higher (system level) goal. This is certainly an area in which VSD could
further develop and provide more guidance.
Acknowledgements This research was supported by Netherlands Organisation for Scientific
Research (NWO) grant MVI-12-E02 on responsible innovation (“maatschappelijk verantwoord
innoveren”). We are indebted to our valorisation committee (Gertjan van den Akker, Theo Borst,
Johan Crols, Michiel Karskens, Gerrit Rietveld, Rick van der Tol, and Gerritjan Valk) for their
insight and comments. We would also like to thank our interviewees: Johan Boekema, Coco Geluk,
Tjakko Kruit, Erik Linschoten, Willem Strabbing, Jeike Wallinga, and Teus de Zwart.
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Chapter 9
Stakeholder Engagement in Policy Development:
Observations and Lessons from International
Experience
Natalie Helbig, Sharon Dawes, Zamira Dzhusupova, Bram Klievink
and Catherine Gerald Mkude
Abstract This chapter provides a starting point for better understanding how
different approaches, tools, and technologies can support effective stakeholder par-
ticipation in policy development. Participatory policy making involves stakeholders
in various stages of the policy process and can focus on both the substance of the
policy problem or on improving the tools and processes of policy development. We
examine five international cases of stakeholder engagement in policy development to
explore two questions: (1) what types of engagement tools and processes are useful
for different stakeholders and contexts? And (2) what factors support the effective use
of particular tools and technologies toward constructive outcomes? The cases address
e-government strategic planning in a developing country, energy policy in a transi-
tional economy, development of new technology and policy innovations in global
trade, exploration of tools for policy-relevant evidence in early childhood decision
making, and development of indicators for evaluating policy options in urban plan-
ning. Following a comparison of the cases, we discuss salient factors of stakeholder
N. Helbig (�) · S. Dawes
Center for Technology in Government, University at Albany, 187 Wolf Road,
Suite 301, 12205 Albany, New York, USA
e-mail: nhelbig@ctg.albany.edu
S. Dawes
e-mail: sdawes@ctg.albany.edu
Z. Dzhusupova
Department of Public Administration and Development Management
United Nations Department of Economic and Social Affairs (UNDESA), New York, USA
e-mail: dzhusupova@un.org
B. Klievink
Faculty of Technology, Policy and Management, Delft University of Technology,
Jaffalaan 5, 2628 BX, Delft, The Netherlands
e-mail: a.j.klievink@tudelft.nl
C. G. Mkude
Institute for IS Research, University of Koblenz-Landau, Universitätsstr. 1,
56070 Koblenz, Germany
e-mail: cmkude@uni-koblenz.de
© Springer International Publishing Switzerland 2015 177
M. Janssen et al. (eds.), Policy Practice and Digital Science,
Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_9
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178 N. Helbig et al.
selection and representation, stakeholder support and education, the value of stake-
holder engagement for dealing with complexity, and the usefulness of third-party
experts for enhancing transparency and improving tools for engagement.
9.1 Introduction
Complex public problems are shared and dispersed across multiple organizations and
domains (Kettl 2002). Consider, for example, the array of concerns associated with
improving air quality or assuring the safety of food products. The formal governmen-
tal responses to these specific public needs are addressed through public policies.
Policy might focus on different geographic locations, processes, or products, or
could specify how certain outcomes are defined, observed, and assessed. Moreover,
individuals, families, communities, industry, and government itself are all affected
by policy choices, and they all have interests in both the decision-making process
and the final decisions (Bryson 2004).
In light of seemingly intractable and complex social problems, public administra-
tors have shifted toward governance activities that allow citizens and stakeholders to
have deeper involvement in the policy-making process and the work of government
(Bingham et al. 2005). Governance models which focus on quasi-legislative activ-
ities such as participatory budgeting, citizen juries, focus groups, roundtables, or
town meetings (Bingham et al. 2005; Fishkin 1995) create opportunities for citizens
and stakeholders to envision their future growth (Myers and Kitsuse 2000), clarify
their own policy preferences, engage in dialogue on policy choices, or bring various
groups to consensus on proposals (McAfee 2004). The models vary based on degree
of involvement by the general population, whether they occur in public spaces, if the
stakeholders are actually empowered, and whether they lead to tangible outcomes
(Bingham et al. 2005).
Stakeholder engagement objectives may also vary by their point of connection
with the policy process (Fung 2006). The policy process is complex and there are
many different ways to conceptualize how it works. The stages heuristic of public
policy making is one of the most broadly accepted (Sabatier 1991). Although the
utility of the stages model has limits, and numerous advances in theories and methods
for understanding the policy process have been made, the stages heuristic continues
to offer useful conceptualizations (Jenkins-Smith and Sabatier 1993). While specifi-
cation and content of the stages vary somewhat throughout the literature, however (as
shown in Fig. 9.1), models often comprise some combination of problem identifica-
tion, agenda setting, formulation, adoption, implementation, and policy evaluation
(Lasswell 1951; Easton 1965; Jones 1977). More recent conceptualizations involve
feedback across the various stages.
Research in both the public and private sectors has identified a number of bene-
fits associated with stakeholder engagement in governance. Stakeholders’ interests
illuminate the multiplicity of factors that underlie policy problems, decisions, and
implementation. Direct engagement of stakeholders increases public understanding
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9 Stakeholder Engagement in Policy Development 179
Fig. 9.1 Stages of the policy process
of the issues and the consequences of different choices. Accordingly, engagement
generates more options for policies or actions. Engagement brings more information
into the deliberation process from different kinds of stakeholders so that decisions are
more likely to avoid unintended consequences and fit better into existing contexts.
Engagement also reveals both conflicts and agreements among different stakeholder
groups. While taking stakeholders into account is a crucial aspect of solving public
problems, policy development includes both powerful and powerless stakeholders
within the process (Bryson 2004). Some stakeholders have the power, knowledge,
or resources to affect the policy content, while others are relatively powerless but
nevertheless are affected, sometimes in dramatic ways (Brugha and Varvasovszky
2000). Thus, open and evenhanded stakeholder engagement, especially among those
with conflicting viewpoints, can sometimes resolve differences and build trust in the
policy-making process and help secure public acceptance of decisions (e.g., Klievink
et al. 2012).
In the past 20 years, specialized technologies, electronic communication, and
advanced analytical, modeling, and simulation techniques have been developed to
support governance processes. Administrators, analysts, and planners must decide
how and when to engage citizens and stakeholders in governance, particularly during
the different stages of policy making. They must also consider which mechanisms
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to use for managing the relationships (Bryson 2004) and must select from a variety
of tools and techniques. In this chapter, we begin to explore two questions: (1)
What types of engagement tools and processes are useful for different stakeholders
and contexts? And (2) what factors support the effective use of particular tools and
technologies toward constructive outcomes?
The next sections start by reviewing the foundational elements of stakeholder the-
ory and its relation to governance, including a summary of tools and techniques used
to identify stakeholders and analyze stakeholder interests and ways to classify types
of engagement. We then offer five case stories of stakeholder engagement in complex
and dynamic settings across the world including e-government strategic planning in a
developing country, exploring different uses of evidence in early childhood decision
making, developing technology and policy innovations in global trade, and involving
citizens in the design of energy policy and transportation planning. The cases vary in
both policy content and the extent to which newer technologies were used to deal with
the complexity of the engagement process, their accessibility and understandability
to outsiders, and the advantages and disadvantages they offer to expert stakeholders
as compared to laymen. We then compare the cases, discuss their similarities and
differences, and conclude with a discussion of the usefulness of different tools and
processes for different stakeholders and contexts and the factors that support their
effectiveness.
9.2 Foundations of Stakeholder Engagement
Stakeholder engagement, as a concept, originated within organizational studies as an
approach to managing corporations (Freeman 2010; Bingham et al. 2005; Donald-
son and Preston 1995; Mitchell et al. 1997). This approach has since been adapted
for use by public sector organizations to highlight the importance of stakeholders in
various aspects of the policy-making process (Bingham et al. 2005). Bingham et al.
(2005) situate stakeholders as part of “new governance” concepts where government
actively involves citizens as stakeholders in decision making through activities such
as deliberative democracy, participatory budgeting, or collaborative policy making.
Research on stakeholder inclusion in government processes has been found to en-
hance accountability, efficiency in decision-making processes, and good governance
(Ackerman 2004; Flak and Rose 2005; Yetano et al. 2010). The growing popularity
of stakeholder analysis reflects an increasing recognition of stakeholder influences
on decision-making processes (Brugha and Varvasovszky 2000).
9.2.1 Defining Stakeholders
The term “stakeholder” is defined differently by different disciplines. Most defini-
tions mention similar stakeholder categories such as companies and their employees
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or external entities such as suppliers, customers, governments, or creditors. In the
public sector, the definition of stakeholder emphasizes categories of citizens defined
by demographic characteristics, life stages, interest groups, or organizational bound-
aries (Bingham et al. 2005; Ackerman 2004; Yetano et al. 2010). Stakeholders can
be both internal to the government (e.g., the government organizations responsible
for policy implementation) and external to it (e.g., the industries, communities, or
individuals to be affected by government actions or rules).
In this chapter, we use Freeman’s (1984) definition of stakeholder as any group
or individual who can affect or is affected by the achievement of an organization’s
objectives. In the public sector, “organization” is understood to mean a government
entity or body with responsibility for public policies or services. In the simplest terms,
those who can affect or may be affected by a policy can be considered stakeholders
in that policy. In traditional expert-based approaches to policy making, the needs of
stakeholders are indirectly addressed by public agencies and acknowledged experts
(Bijlsma et al. 2011; De Marchi 2003). In these expert-based approaches, internal
and external stakeholders may be consulted, but in participatory approaches, stake-
holders are not only consulted but are also involved in a structured way to influence
problem framing, policy analysis, and decision making. Bijlsma et al. (2011) define
participatory policy development as the “influence of stakeholder involvement on the
development of substance in policy development, notably the framing of the policy
problem, the policy analysis and design, and the creation and use of knowledge”
(p. 51).
9.2.2 Stakeholder Identification and Analysis
Stakeholder identification and analysis is an important first phase in stakeholder en-
gagement processes (Freeman 2010). Analysis typically involves five steps (Kennon
et al. 2009): identifying stakeholders, understanding and managing stakeholders,
setting goals, identifying the costs of engagement, and evaluating and revisiting the
analysis. Through these various steps, an analysis helps to distinguish stakeholders
from non-stakeholders and to identify the ways that stakeholders need to be engaged
during different parts of the policy cycle. Over time, the mix of stakeholders in a
particular policy issue is likely to change, as new stakeholders may join the engage-
ment activities, while others may drop out (Elias et al. 2002) or shift among different
types. Joining, dropping out, or moving among types thus dynamically changes the
configuration and analysis of stakeholders over time.
Various techniques for stakeholder identification and analysis are reviewed in
the literature. These techniques focus attention on the interrelations of groups or
organizations with respect to their interests in, or impacts on policies within, a
broader political, economic, and cultural context. These techniques also provide
ways for analysts to understand stakeholder power, influence, needs, and conflicts
of interest. Bryson (2004) characterized stakeholder identification as an iterative
process highlighting the need to determine the purpose of involving stakeholders
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182 N. Helbig et al.
and cautioning that these purposes may change over time. He describes a stage
approach to selecting stakeholders: someone or a small group responsible for the
policy analysis develops an initial stakeholder list as a starting point for thinking
about which stakeholders are missing. Brainstorming and the use of interviews,
questionnaires, focus groups, or other information-gathering techniques can be used
to expand the list. Bryson (2004) notes “this staged process embodies a kind of
technical, political, and ethical rationality” (p. 29). He also lists a variety of analysis
techniques, such as power and influence grids (Eden and Ackermann 1998), bases of
power diagrams (Bryson et al. 2002), stakeholder–issue interrelationship diagrams
(Bryant 2003), problem-frame stakeholder maps (Anderson et al. 1999), ethical
analysis grids (Lewis 1991), or policy attractiveness versus stakeholder capability
grids (Bryson et al. 1986). Each of these tools is used in different situations to help
understand and identify various aspects of stakeholder interests.
9.2.3 Stakeholder Engagement
Stakeholder engagement methods are the means by which stakeholder views, infor-
mation, and opinions are elicited, or by which stakeholders are involved in decision
making. Engagement can take various forms. The International Association for Pub-
lic Participation identified five levels of stakeholder engagement: (IAP2 2007). At
the simplest level, informing, stakeholders are merely informed, for example, via
websites, fact sheets, newsletters, or allowing visitors to observe policy discussions.
The level of engagement in this form is very low and suitable only to engage those
stakeholders with low urgency, influence, importance, or interest (Bryson 2004).
Various methods are available for consulting, including conducting interviews, ad-
ministering surveys to gather information, opening up draft policy documents for
public comment, or using Web 2.0 tools to gather ideas. The main goal of this form
of engagement is to elicit the views and interests, as well as the salient information
that stakeholders have with regard to the policy concern.
Involving stakeholders is a more intensive engagement where stakeholders work
together during the policy development process. Some tools used to ensure that ideas,
interests, and concerns are consistently understood and addressed include scenario
building (Wimmer et al. 2012), engaging panels of experts such as the Delphi method
(Linstone and Turoff 1975), or group model building that includes simulating policy
choices, games, or role playing (Andersen et al. 2007; Vennix et al. 1996). Models,
simulations, or scenarios can be used as boundary objects (Black and Andersen
2012; Star and Griesemer 1989) to enable diverse sets of stakeholders to have a
shared experience and to exchange localized or specialized knowledge in order to
learn, create common understanding, and identify alternative choices. All these levels
focus on the flow of information among actors, but the direction and intensity vary.
The most intense engagement is realized through full collaboration with or even
empowerment of stakeholders. In the IAP2 spectrum of public participation, collab-
oration means stakeholders’ advice and recommendations will be incorporated in
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the final decisions to a maximum extent (IAP2 2007). Empowerment means that the
final decision making is actually in the hands of the public. Realistically, collabora-
tion and empowerment exist within institutional and legal parameters. For example,
the policy-making body (usually a government agency) will need to put some con-
straints or boundaries around the policy options that comport with the limits of its
legal authority. For both levels, consensus-building approaches are essential. This
can be done through citizen juries (Smith and en Wales 2000), the enactment of a
stakeholder board (urbanAPI1; Klievink et al. 2012), or by setting up living labs
(Tan et al. 2011; Higgins and Klein 2011) in which stakeholders collaboratively
develop, implement, and evaluate solutions within a given context. All of these
approaches not only assist in incorporating stakeholders’ views into the policy pro-
cess but also enhance acceptance by stakeholders because they were part of the
deliberation process (e.g., see Klievink and Lucassen 2013).
9.3 Cases
Below we offer five case stories about stakeholder engagement in policy making.
The cases were recommended by a diverse set of eGovPoliNet consortium partners
who shared an interest in tools and techniques to support the policy process. The
main goal of the case stories is to highlight the roles that stakeholders can play in
policy development and to discuss how different methods, tools, and technologies
could be used for engaging stakeholders in the policy process. Each case describes a
situation where stakeholders were involved in the problem definition, agenda setting,
and formulation stages of the policy cycle. In all cases, a trusted third party, generally
university researchers, facilitated the process and applied the tools. The cases vary in
policy content and in the extent of technology use in the engagement process. They
represent different policy domains, and governments at different stages of develop-
ment with different political systems. The first three cases focus on substantive policy
choices for e-government strategic planning, alternative energy policy, and global
trade inspection. The last two concentrate on stakeholder involvement in improving
tools to support the policy-making process. Of those, the first focuses on connect-
ing policy makers and modelers in building a supportive framework for assessing
early childhood programs and second involves stakeholders in defining assessment
indicators to be built into a model that supports urban planning decisions.
In this section, we describe these diverse situations as the foundation for the
comparison presented in Sect. 9.4 where we identify similarities and differences that
suggest approaches, tools, and techniques that are useful and effective in different
contexts and with different kinds of stakeholders.
For each case below, we present the key characteristics of the policy-making situ-
ation and assess the purpose of stakeholder engagement. With respect to stakeholder
1 UrbanAPI is an EC FP7 project focused on interactive analysis, simulation, and visualization
tools for agile urban policy implementation http://www.urbanapi.eu/.
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184 N. Helbig et al.
identification and analysis, we cover both the identification of stakeholders (types)
involved and the methods used for identification and analysis. With respect to stake-
holder engagement (see Sect. 9.2.3), we analyze the engagement approach followed
in each case, as well as the type of participation and the methods of stakeholder en-
gagement. We also inventory which tools and technologies were used and describe
the results and outcomes of each engagement process.
9.3.1 E-Government Strategic Planning in Afghanistan
The EGOV.AF project was a joint initiative of the Afghanistan Ministry of Commu-
nications and Information Technology (MCIT) and the United Nations University–
International Institute for Software Technology–Center for Electronic Governance
(UNU-IIST-EGOV). One goal of EGOV.AF was to develop a nationally owned
EGOV strategy and program (Dzhusupova et al. 2011). In many developing countries,
two major challenges to long-term sustainability of e-government initiatives exist:
(1) too much reliance on donor funding (Ali and Weerakkody 2009) and (2) lack
of understanding regarding citizen demand for e-government services (Basu 2004).
To mitigate these challenges, a strategy of the EGOV.AF project was to reach out
to stakeholders in a systematic way before putting together a national e-government
policy. Afghanistan is one of the poorest countries in the world (World Bank 2012)
plagued by a recent history of war and conflict, with a significant digital divide
between rural and urban areas. Thus, identifying important stakeholders and under-
standing their interests, expectations, capacity, and influence were very important,
but also very difficult.
In 2011, the UNU-IIST-EGOV team engaged in action research with the MCIT
through the development of a stakeholder analysis tool and execution of a series of
stakeholder identification exercises, analyses, and workshops. The MCIT was the
project owner and lead agency, while the UNU-IIST-EGOV provided mentorship,
additional experience, expertise to apply stakeholder analysis tools and engagement
methods, and capacity to facilitate the process.
Historically, standard exercises at the MCIT around e-government planning had
focused only on consultation with technology stakeholders, such as consulting com-
panies. Initially, the MCIT did not see the value in involving citizens, local provinces,
international organizations, academics, or nonprofit organizations that focus on gov-
ernance. The case was made by UNU to engage people outside of government to
address several factors: Many of the nonprofit organizations are advocates for trans-
parency and good governance, donor organizations assert influence over the process
through special programs and funding, and the provincial governments work closely
and most directly with citizens.
To expand MCIT’s limited understanding of this broad set of stakeholders, they
conducted a series of consultation and involvement activities. The first instance of
engagement with stakeholders was a survey that asked questions about their inter-
ests, needs, activities, and conditions. The team also collected additional contextual
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information from websites and professional contacts. The second stage of engage-
ment occurred after the analysis of the survey. Using the stakeholder analysis tool
developed by UNU, the MCIT identified from the survey results a set of interested
and relevant stakeholders, defined the roles for major stakeholders in the policy pro-
cess, and developed communication strategies. Later these stakeholders were invited
to attend two stakeholder workshops. One workshop was designed as a “visioning”
exercise and another designed to elicit “strategy development.” During the work-
shops, MCIT and UNU-IIST-EGOV were able to provide participants with general
knowledge about approaches and methodologies regarding strategy development,
provided examples from other countries, and facilitated discussions focused on e-
government in the local Afghanistan context. Participants in the workshops were
encouraged to share their ideas and to discuss and prioritize strategic goals and tasks
for e-government based on the mutual consensus among them. The last stage of
the stakeholder engagement was to complete a series of face-to-face meetings and
e-mails in which the MCIT collected suggestions on strategic actions. Additional
feedback was taken through an e-forum set up on the government website to collect
comments on a draft national strategy.
The key result of the overall project was the successful completion of a nationally
owned EGOV vision and strategy agreed upon by most important stakeholders. The
most critical points of the vision and strategy were to better respond to Afghan
citizens’ expectations that e-government would bring convenient public services,
transparency, accountability, and responsiveness and would help to deter widespread
corruption. The project provided evidence that stakeholder engagement in national-
level planning processes was possible, and that involving stakeholders can increase
commitment, build consensus, and demonstrate transparency and openness in the
strategic e-government planning process.
9.3.2 Renewable Energy Policy for Kosice, Slovakia
The process of developing an energy policy in Kosice self-governing region (KSR)
in Slovakia is surrounded by political, economic, and environmental challenges.
High dependency on imported energy from Russia and Ukraine, presented KSR
with economic and political vulnerabilities. The emergence of domestic small to
medium enterprises (SMEs) within the energy sector has provided new opportunities
for employment and new technologies for utilizing local energy sources. Control
of energy production with respect to emissions also impacted the policy-making
environment. Any change in the sources of energy would likely affect the pricing of
energy consumption and directly affect citizens and businesses. This case not only
is a matter for policy makers and the authorities devising new energy policies but
also affects the KSR government entities, energy importing companies, local SMEs,
and citizens. Creating a new policy in such an environment required considerations
of a wide variety of stakeholders; the goal was to ensure the new policy would be
realistic, supported, and agreed upon.
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This case describes a pilot of the Open Collaboration for Policy Modeling
(OCOPOMO) project.2 The main objective of the OCOPOMO project was to de-
velop an online environment for, and information and communications technology
(ICT) tools for, policy modeling in collaboration with stakeholders (Wimmer et al.
2012). Presenting complex information on policy choices for renewable energy re-
quires some technical expertise and is influenced by individual beliefs. The pilot
project in Kosice focused on capturing stakeholders’ views on alternative renewable
sources of energy versus traditional energy production and consumption. It provided
an understanding of various choices in relation to different policies for promoting the
use of renewable energy, the perceived market potential for different energy sources,
barriers hindering different kinds of energy generation in the region, and the moti-
vating factors leading citizens and companies towards renewable energy sources. It
also provided an early understanding of employment, financial, and environmental
impacts of any potential policy (Furdík et al. 2010). This pilot was the first time that
Kosice used advanced ICTs in policy making and the first time the region involved
a range of stakeholders other than policy makers, experts, and key representatives
from private heat producers and distribution companies.
The project team met with regional government committees and identified and
analyzed relevant stakeholders ranging from heating producers to distribution compa-
nies, building construction experts to technology experts, to household associations,
citizens, and city employees. Desk research and surveys were used to identify the
stakeholders, their roles, and expectations in the engagement process. The local au-
thorities were mainly responsible for identifying the stakeholders. The project team
and the local government applied action research to engage these stakeholders in
the process and involvement was by invitation only. Several methods of engagement
were used. Workshops were used to clarify tasks and expectations of stakeholders in
the engagement process. Collaborative scenario development enabled stakeholders to
provide evidence documents and to generate scenarios related to the policy problem.
This method also allowed stakeholders to collaborate among themselves by exchang-
ing views and concerns about the policy problem and possible solutions. Conceptual
modeling transformed stakeholder-generated scenarios and evidences into formal
policy models for simulation and then transformed the model-based scenarios into
narrative scenarios to enable understanding of simulation results to stakeholders and
steer further collaboration on the results. This process was iterative as new scenarios
emerging from the discussions of results could be evaluated and simulated again.
The stakeholders first met with the project team and were given a tutorial of how
the OCOPOMO online platform is used and they were free to use the platform for
about 1 month. The online platform provided background and supporting materials
to inform stakeholders of the different policy options available. After reviewing
existing options, stakeholders could propose several scenarios—for example, they
could propose a type of renewable energy and discuss what should be done from
the stakeholder’s own perspective. Scenarios, based on these stakeholder proposals,
2 http://www.ocopomo.eu/in-a-nutshell/piloting-cases/kosice-self-governing-region-slovakia.
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9 Stakeholder Engagement in Policy Development 187
were later turned into formal policy models for simulation. The consistent conceptual
description (CCD) tool was used to perform this task.
The next phase began almost 1 year later with another face-to-face meeting to
inform stakeholders of the purpose of the second iteration. Given the length of
time between the first exercise and the second, some stakeholders were involved
in the first face-to-face one but not the second, and some started in the second. In
the second iteration, stakeholders were presented with simulation results of their
policy choices. Additional background documents were provided to help educate
them such as a return on investment (ROI) of different energy sources proposed.
Stakeholders, particularly policy owners, provided comments on the model-based
scenarios and then published one new evidence-based scenario. The topics which
were most discussed leading to the new scenario included detailed technical pros
and cons of a local versus central heating system, ROIs, legislation proposed by
heat producers that would affect customers who decided to disconnect from the
central heating system, and financial tools for investments in building renovation or
installation of new heat sources.
The project was successful in highlighting the need for and usefulness of more
innovative approaches to policy development processes. These innovative approaches
proved to be particularly important with diverse stakeholders with different interests
in an existing problem and potential solutions (Wimmer et al. 2012). The added
value of OCOPOMO to traditional approaches is the added confidence for policy
makers about the expected outcomes of a policy in respect to stakeholders involved.
Moreover, the stakeholder engagement process in Kosice was positively viewed by
the stakeholders themselves. It enabled better understanding of the policy problem
through background documents provided in the platform, and it also provided a tool
where different stakeholders’ views and expectations could be explicitly captured.
9.3.3 Redesigning the European Union’s Inspection Capability
for International Trade
The European Union (EU) is implementing a risk-based approach (RBA) policy
to government supervision of international trade lanes. As part of this approach,
the risk posed by cargo entering and leaving the EU is analyzed on the basis of
cargo information submitted electronically in a single declaration by operators prior
to departure or arrival. However, this policy can only be effective if the data that
circulate among the supply chain partners are accurate, timely, and of sufficient
quality to be relied upon, which is currently not the case (Hesketh 2010). This case
draws from two projects: Extended Single Window (ESW): Information Gateway
to Europe, funded by the Dutch Institute for Advanced Logistics (DINALOG), and
common assessment and analysis of risk in global supply chains (CASSANDRA),
funded by the 7th Framework Program of the European Commission. The goal of
both projects was to improve supply chain visibility.
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188 N. Helbig et al.
Transparency is important for both government and commercial interests; it re-
lates to having access to the transaction data necessary to know what is actually
happening in the supply chain. However, major challenges exist in today’s global
supply chains, including lack of trust and understanding between public and private
entities and among private entities (Klievink et al. 2012) about existing laws and
ways of working among EU countries and other countries. Without the involvement
of international trading businesses and other stakeholders, and without their active
contribution to data sharing solutions that enable the RBA policy, the policy will not
lead to the intended results for government and may lead to unnecessary increases
in the administrative burden of legitimate traders.
To overcome these challenges, the project team assembled an international consor-
tium of government bodies that included multiple European customs organizations,
in addition to universities, IT providers, logistics operators, and standardization bod-
ies. The project team conducted desk research and a survey based on Bryson (2004)
to elicit stakeholders’ interests, urgency, influence, and importance. The total number
of entities involved in international supply chains is so large that it was necessary to
choose stakeholders that would reasonably represent the range of actors. Therefore,
selection was based on criticality and representativeness. For example, the con-
sortium involved representatives of a several very large and medium-sized freight
forwarders. This was done to ensure different perspectives within this stakeholder
group without having to involve the hundreds of parties that can be involved with the
cargo on any single ship. Stakeholders were also grouped according to trade lanes.
This approach limited the total number of actors by using the trade lane as a boundary.
To ensure diversity in interests, ten different global trade lanes were modeled, in-
cluding lanes between Shenzhen (China) and Felixstowe (UK), Penang (Malaysia)
and Rotterdam (the Netherlands), Alexandria (Egypt) and Barcelona (Spain), and
Bremerhaven (Germany) and Charleston (USA). Using this method, the stakehold-
ers were able to see the common themes across trade lanes that are important for
each of the key stakeholder groups.
In order to engage stakeholders to innovate within a real-life setting, a living lab
approach was used. Tan et al. (2011) describe a living lab methodology as bringing
together multiple stakeholders, across multiple locations, and seeing stakeholders
as co-innovators. A living lab methodology is suitable for situations where a neutral
party, often academics, acts as honest brokers to bring the different stakeholders to
consensus. Each living lab group used real trade lanes to model the physical flow
of data, information system landscape, and administrative burden in order to config-
ure, demonstrate, and refine the entire system with the stakeholders. The consortium
team created visual models and data-flow diagrams of the existing and to-be situa-
tions to enable the stakeholders to sort out the policy and data-sharing issues among
themselves. Another goal was for stakeholders to come to common understanding of
their respective situations, ultimately joining up different systems of different stake-
holders in order to capture the data they collectively needed. The overall dataset was
visualized in a dashboard with role-based access. The dashboard enabled discussion
of how the system would impact the day-to-day processes of the various businesses
and inspection authorities.
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Involving stakeholders early helped increase commitment and consensus to this
initiative. However, decision making remained relatively slow due to the consider-
able time it takes to design technical tools, models, and diagrams, and to constantly
update them to reflect the feedback from stakeholders’ advice and recommendations.
By providing a comprehensive overview of the roles, the data sources, and the work
processes using them, parties came to an understanding of how the innovations were
used. Through this, they over time build trust towards those potential vulnerabilities
that the innovation might bring, would not be exploited. This facilitated acceptance
and uptake by the various stakeholder groups. In addition, not all of the potential
answers the living lab groups provided are also enabled by existing European legis-
lation. Alignment between the business stakeholder groups, national governments,
and European bodies is still needed. One of the outcomes of the project is therefore
a consensus-based agenda for further policy development.
9.3.4 Understanding Child Health Outcomes in New Zealand
The next case examines the Modelling the Early Life-Course (MEL-C) project in
New Zealand, which was supported by a public good research grant provided to
researchers at the University of Auckland, New Zealand (Milne et al. 2014). Life-
course studies examine “the biological, behavioral and psychosocial pathways that
operate across an individual’s life course, as well as across generations, to influence
the development of chronic diseases” (Ben-Shlomo and Kuh 2002). An abundance
of research evidence can be found about the early life course of children and the
determinants of health. The goal of the project was to develop a decision support
software tool for policy makers to test different policy scenarios against realistic
data and to consider this evidence alongside other policy-relevant information such
as politics, other evaluations, or expert consultations. The main purpose was not to
develop a specific policy but to develop a process and tool for better identification
and use of data in this policy domain.
In an environment where a great deal of information about a policy exists, the tool
is meant to help bridge the research–policy translation gap (Milne et al. 2014). The
lack of research evidence uptake by policy makers is well documented (Lomas 2007;
Van Egmond et al. 2011). One main factor is the lack of uptake in the “translation
gap”—characterized as the mismatch between the knowledge that research produces
and the knowledge that policy makers want (Milne et al. 2014). Milne et al. (2014)
identify two solutions to bridge the gap—knowledge brokers (Frost et al. 2012;
Knight and Lightowler 2010; Lomas 2007) and research–policy partnerships (Best
and Holmes 2010; Van Egmond et al. 2011). Knowledge brokers act as translators,
turning the research evidence into information that is easily understood and usable
by policy makers. Research–policy partnerships involve a more intense interaction
between both groups, where they work together to produce the evidence needed for
policy purposes. Previous work focused on database interventions aimed at knowl-
edge translation where all relevant documents synthesizing research results could be
w.jager@rug.nl

190 N. Helbig et al.
found (Milne et al. 2014). However, with the online databases the onus is still on
policy makers to search for relevant papers, assess their content for relevance, and
evaluate their importance for the policy question under consideration. The MEL-C
project took a different approach with a decision-support tool “where the evidence
is embedded in a working model and can be interrogated to address specific policy
questions” (p. 8).
Using a micro-simulation model, the tool incorporates longitudinal data to
determine the normal transition of children through their life course and the im-
pact of policy interventions on their outcomes. Two representatives each from
four New Zealand government ministries—Health, Education, Justice, and Social
Development—formed a “policy reference group” for the project (Milne et al. 2014).
The representatives were selected because they represented people who could under-
stand the aims of the project and were data and technology savvy. Thus, the boundary
for engagement was limited to the translation gap, and did not extend to the behavior
of the children modeled within the system. The main strategy for involving policy
makers was to hold regular, face-to-face meetings for almost 2 years to discuss the
development of the MEL-C tool, including the simulation model and graphical user
interface. The discussions were facilitated and documented by the task leader for
end-user engagement.
The simulation model was shown to stakeholders who then provided feedback and
became collaborators in the development of user interfaces and the types of key policy
questions that the model needed to be able to address. The results of this specialized
form of stakeholder engagement included a much more useful decision-support tool
than might have been developed otherwise, an ongoing process of collaborative
refinement, and a set of potential users and advocates for the tool.
Results of the model are beginning to be explored. For example, for child health
service use outcomes it was found that appreciable improvement was only effected
by modifying multiple determinants; structural determinants (e.g., ethnicity, family
structure) were relatively more important than intermediary determinants (e.g., over-
crowding, parental smoking) as potential policy levers; there was a social gradient
of effect; and interventions bestowed the greatest benefit to the most disadvantaged
groups with a corresponding reduction in disparities between the worst-off and the
best-off (Lay-Yee et al. 2014).
9.3.5 Transportation and Urban Planning Indicator Development
in the USA
Understanding how choices today will impact life in the future is a major concern
for policy making in any area. In transportation and urban planning, it is even more
important because the infrastructure created is not easily changed, once roads and
buildings are built and patterns of living start to evolve around them. The urban plan-
ning context is fraught with different stakeholders who often have fundamentally
opposing beliefs and value systems (Pace 1990; Borning et al. 2005). They embody
w.jager@rug.nl

9 Stakeholder Engagement in Policy Development 191
widely divergent opinions regarding urban development and land use. Each stake-
holder group is likely to have their own philosophies about different forms of land
use in urban environments, and different views about how long-term planning should
occur, what situations constitute problematic conditions, what solutions should be
sought for those problems, and what constitutes successful outcomes.
Under these contentious conditions, advanced computer simulation tools that
show the long-term potential effects of different choices can contribute to legitimation
of the policy process as well as to well-considered decisions. However, in order to
achieve this, the model itself must be considered legitimate. In other words, its
structure, inputs, processes, and outputs must be transparent and understandable to
all stakeholders. Our last case, UrbanSim, is a land-use modeling system, developed
over the past 20 years, that helps policy makers and stakeholders understand the
20–30-year impacts of different choices regarding land use and transportation on
community outcomes including effects on the economy and the environment. It has
been used widely in the USA and Europe and is of growing interest globally. The
system not only estimates the direct effects of different infrastructure and policy
choices but also estimates how individual and group responses to those choices will
affect the outcomes (Borning et al. 2005; Borning et al. 2008).
UrbanSim simulation results are mainly presented to users as indicators. These
indicators are variables that convey information about an attribute of the system at a
given time. Indicators in UrbanSim include such variables as the population density
in different neighborhoods, the ratio of car trips to bus trips for the region, and
the projected cost of land per acre in different parts of the region. These and other
indicators are presented under different possible scenarios over the course of the
full simulation, generally 30 years. Indicator values are presented in tables, graphs,
charts, or maps (Friedman et al. 2008). These indicators allow stakeholders to assess
and compare the results of different policy scenarios on a consistent set of dimensions.
For example, if a city has the goal of supporting more walkable densely populated
urban neighborhoods as an alternative to sprawl surrounding the city center, then
changes in the “population density” indicator in different neighborhoods could be
used to assess the simulated outcomes of different policies over time (Borning et al.
2005).
In recent years, enhancements to UrbanSim have concentrated on making the
model more realistic and meaningful to stakeholders by expanding, categorizing,
and differentiating the stakeholder values represented by the indicators. The Urban-
Sim team had two goals: to make advocacy for different views more explicit and
contextualized, and to improve the overall legitimacy of the system by incorporating
these values in a wider range of indicators in the simulations. The involvement of
stakeholders, essentially a process of codevelopment of the model, was guided by
an overarching theory of value sensitive design (Friedman 1997). A key feature of
value sensitive design is designing technology that accounts for human values with
an emphasis on representing direct and indirect stakeholders (Borning et al. 2005).
The UrbanSim team partnered with three local organizations in the Seattle, Wash-
ington, region to develop and test new ways of expressing their values to model users
through the choice of indicators and related technical information. The partners (a
w.jager@rug.nl

192 N. Helbig et al.
government agency, a business association, and an environmental group) were se-
lected to represent a range of known issues and stakeholder views about development
in the region. The goal was to create for each group a narrative value indicator per-
spective that explained the values of most importance to that group and to select,
define, and incorporate key indicators representing those views in the model. Stake-
holders were convened in separate groups so that they could work independently to
formulate their indicator perspectives. This was an important design choice because
the goal was to present each group’s values and desires by essentially telling a story
advocating particular values and criteria for evaluating policy outcomes (Borning
et al 2005). The team engaged each stakeholder group through a series of face-
to-face meetings and semi-structured interviews to help them craft and write both
narratives and descriptions of indicators that closely matched their core values and
views.
To assess the extent to which these approaches enhanced the legitimation of the
model, a separate group of citizen evaluators reviewed each grouping of stakeholder-
selected indicators and along with associated technical documentation as well as the
indicators in the system as a whole. They considered coherence, informativeness,
usefulness for supporting diverse opinions, usefulness for advocating for differing
views and values, and usefulness for supporting the democratic process. The evalu-
ation showed positive scores on all measures and also produced additional findings
about the usefulness of different kinds of information (technical compared to advo-
cacy), the importance of explicitly presenting and balancing diverse views, and the
overall perception of transparency and lack of bias in the modeling system itself.
9.4 Case Comparison
Table 9.1 presents key elements of each case story based on the following points
of comparison: (a) situation and approach, (b) types of stakeholders and type of
participation, (c) methods for stakeholder identification, (d) methods for stakeholder
engagement, (e) tools and technologies used, and (f) results.
w.jager@rug.nl

9 Stakeholder Engagement in Policy Development 193
T
ab
le
9.
1
C
as
e
co
m
pa
ri
so
n
C
as
e
1
C
as
e
2
C
as
e
3
C
as
e
4
C
as
e
5
P
ol
ic
y
ar
ea
E
-g
ov
er
nm
en
t
R
en
ew
ab
le
en
er
gy
In
te
rn
at
io
na
l
tr
ad
e
C
hi
ld
he
al
th
U
rb
an
pl
an
ni
ng
L
en
gt
h
of
pr
oj
ec
t
1
ye
ar
3
ye
ar
s
3
ye
ar
s
5
ye
ar
s
1
ye
ar
P
ri
m
ar
y
co
un
tr
y
(i
es
)
A
fg
ha
ni
st
an
S
lo
va
ki
a
E
ur
op
ea
n
U
ni
on
an
d
tr
ad
in
g
pa
rt
ne
rs
N
ew
Z
ea
la
nd
U
S
A
D
ev
el
op
m
en
t
st
at
us
D
ev
el
op
in
g
T
ra
ns
it
io
n
D
ev
el
op
ed
an
d
de
ve
lo
pi
ng
D
ev
el
op
ed
D
ev
el
op
ed
L
ev
el
of
go
ve
rn
m
en
t
N
at
io
na
l
M
un
ic
ip
al
M
ul
ti
na
ti
on
al
N
at
io
na
l
R
eg
io
na
l
A
pp
ro
ac
h
A
ct
io
n
re
se
a
rc
h

in
vo
lv
in
g
tr
us
te
d
3r
d
pa
rt
y
fa
ci
li
ta
te
s
ne
w
co
nn
ec
ti
on
s
be
tw
ee
n
st
ak
eh
ol
de
rs
an
d
go
ve
rn
m
en
t
A
ct
io
n
re
se
a
rc
h

in
vo
lv
in
g
co
ll
ab
or
at
iv
e
sc
en
ar
io
bu
il
di
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th
ro
ug
h
an
on
li
ne
to
ol
,
su
pp
le
m
en
te
d
w
it
h
in
-p
er
so
n
m
ee
ti
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s
A
ct
io
n
re
se
a
rc
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cr
ea
ti
on
of
a
li
vi
ng
la
b
w
he
re
st
ak
eh
ol
de
rs
th
em
se
lv
es
,f
ac
il
it
at
ed
by
3r
d
pa
rt
ie
s,
de
ve
lo
pe
d
so
lu
ti
on
s
an
d
im
pl
em
en
t
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em
R
es
ea
rc
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p
ra
ct
ic
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p
a
rt
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er
sh
ip

re
se
ar
ch
er
s
an
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po
li
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m
ak
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w
or
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to
ge
th
er
th
ro
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h
it
er
at
iv
e
di
sc
us
si
on
,
de
m
on
st
ra
ti
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,a
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ha
nc
em
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A
ct
io
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re
se
a
rc
h

us
in
g
va
lu
e-
se
ns
it
iv
e
de
si
gn
w
he
re
st
ak
eh
ol
de
r
va
lu
es
ar
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m
ad
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ex
pl
ic
it
in
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de
ve
lo
pm
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t
of
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ha
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em
en
ts
to
th
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ch
no
lo
gy
an
d
m
od
el
sy
st
em
P
ur
po
se
of
st
ak
eh
ol
de
r
en
ga
ge
m
en
t
E
ns
ur
e
ow
ne
rs
hi
p,
co
m
m
it
m
en
t,
an
d
tr
an
sp
ar
en
cy
,i
n
pu
rs
ui
t
of
b
a
la
n
ci
n
g
st
a
ke
h
o
ld
er
s’
in
te
re
st
s
B
u
il
d
co
n
se
n
su
s
to
su
pp
or
t
a
re
al
is
ti
c
po
li
cy
th
at
w
ou
ld
be
w
id
el
y
ac
ce
pt
ed
A
tt
un
e
th
e
sy
st
em
to
w
ar
ds
th
e
in
te
re
st
s
an
d
ex
is
ti
ng
pr
ac
ti
ce
s
of
th
e
st
ak
eh
ol
de
rs
,t
he
re
by
bu
il
d
in
g
co
m
m
it
m
en
t
a
n
d
su
p
p
o
rt
in
g
co
n
se
n
su
s
a
m
o
n
g
st
a
ke
h
o
ld
er
s
to
n
ew
p
o
li
ci
es
F
a
ci
li
ta
te
sy
n
th
es
is
o
f
re
se
a
rc
h
fi
n
d
in
g
s
a
n
d
im
p
ro
ve
th
e
u
se
fu
ln
es
s
an
d
u
sa
b
il
it
y
o
f
a
d
ec
is
io
n
-s
u
p
p
o
rt
to
o
l
fo
r
po
li
cy
m
ak
er
s
E
n
h
a
n
ce
th
e
le
g
it
im
a
cy
o
f
a
m
o
d
el
in
g
sy
st
em
us
ed
in
co
nt
en
ti
ou
s
po
li
cy
ar
ea
s
w.jager@rug.nl

194 N. Helbig et al.
T
ab
le
9.
1
(c
on
ti
nu
ed
) C
as
e
1
C
as
e
2
C
as
e
3
C
as
e
4
C
as
e
5
S
ta
ke
ho
ld
er
ty
pe
s
in
vo
lv
ed
R
ep
re
se
nt
at
iv
es
fr
om
ce
nt
ra
l
go
ve
rn
m
en
t,
lo
ca
l
go
ve
rn
m
en
ts
,
pu
bl
ic
se
rv
ic
e
pr
ov
id
er
s,
IT
an
d
co
ns
ul
ti
ng
fi
rm
s,
N
G
O
s,
un
iv
er
si
ti
es
,
th
in
k
ta
nk
s,
re
so
ur
ce
ce
nt
er
s;
in
te
rn
at
io
na
l
or
ga
ni
za
ti
on
s
(d
on
or
s
an
d
sp
on
so
rs
)
P
ol
ic
y
m
ak
er
s,
re
pr
es
en
ta
ti
ve
s
fr
om
en
er
gy
-r
el
at
ed
co
m
pa
ni
es
,e
xp
er
t
gr
ou
ps
,r
ep
re
se
nt
at
iv
es
fr
om
ci
ti
ze
ns
an
d
ho
us
in
g
as
so
ci
at
io
ns
In
vo
lv
em
en
t
of
“e
xe
m
pl
ar
y”
ac
to
rs
fr
om
m
ai
n
st
ak
eh
ol
de
r
gr
ou
ps
:
go
ve
rn
m
en
t,
in
te
rn
at
io
na
l
tr
ad
er
s,
IT
so
lu
ti
on
pr
ov
id
er
s,
st
an
da
rd
s
or
ga
ni
za
ti
on
s
E
xp
er
t
gr
ou
p
dr
aw
n
fr
om
pu
bl
ic
ag
en
ci
es
re
sp
on
si
bl
e
fo
r
ch
il
dr
en
’s
he
al
th
R
ep
re
se
nt
at
iv
es
of
se
le
ct
ed
no
np
ro
fi
t,
go
ve
rn
m
en
t,
an
d
bu
si
ne
ss
in
te
re
st
s
kn
ow
n
to
ha
ve
st
ro
ng
vi
ew
s
of
de
ve
lo
pm
en
t
in
th
e
re
gi
on
M
et
ho
d
fo
r
id
en
ti
fy
in
g
st
ak
eh
ol
de
rs
O
nl
in
e
su
rv
ey
s;
in
te
rv
ie
w
s;
an
al
ys
is
of
in
te
re
st
s,
ne
ed
s,
an
d
ca
pa
bi
li
ti
es
D
es
k
re
se
ar
ch
,s
ur
ve
y
re
se
ar
ch
,q
ua
li
ta
ti
ve
an
d
qu
an
ti
ta
ti
ve
da
ta
an
al
ys
is
.F
ac
e-
to
-f
ac
e
m
ee
ti
ng
s
D
et
ai
le
d
st
ak
eh
ol
de
r
m
ap
fo
r
sp
ec
ifi
c
tr
ad
e
la
ne
s
(i
nc
lu
di
ng
co
m
m
er
ci
al
,
go
ve
rn
m
en
t,
lo
gi
st
ic
s,
an
d
in
fo
rm
at
io
n
fu
nc
ti
on
s)
C
on
ve
ni
en
ce
sa
m
pl
e
of
po
li
cy
m
ak
er
s
in
th
e
do
m
ai
n
kn
ow
n
to
th
e
de
ve
lo
pe
rs
C
on
ve
ni
en
ce
sa
m
pl
e
of
or
ga
ni
za
ti
on
s
kn
ow
n
to
re
pr
es
en
t
a
ra
ng
e
of
vi
ew
s
ab
ou
t
ur
ba
n
de
ve
lo
pm
en
t
in
th
e
re
gi
on
T
yp
e
of
pa
rt
ic
ip
at
io
n
In
vo
lv
in
g
In
vo
lv
in
g
In
vo
lv
in
g/
co
ll
ab
or
at
io
n
In
vo
lv
in
g
In
vo
lv
in
g
M
et
ho
d
of
st
ak
eh
ol
de
r
en
ga
ge
m
en
t
F
ac
e-
to
-f
ac
e
w
or
ks
ho
ps
F
ac
e-
to
-f
ac
e
w
or
ks
ho
ps
;
co
ll
ab
or
at
iv
e
sc
en
ar
io
bu
il
di
ng
F
ac
e-
to
-f
ac
e
m
ee
ti
ng
s;
co
ns
en
su
s-
bu
il
di
ng
w
or
ks
ho
ps
;
in
te
rv
ie
w
s,
jo
in
t
sp
ec
ifi
ca
ti
on
of
tr
ad
e
la
ne
an
d
of
so
lu
ti
on
F
ac
e-
to
-f
ac
e
m
ee
ti
ng
s
be
tw
ee
n
de
ve
lo
pe
rs
an
d
po
li
cy
m
ak
er
/u
se
rs
S
ep
ar
at
e
fa
ce
-t
o-
fa
ce
m
ee
ti
ng
s,
in
te
rv
ie
w
s,
jo
in
t
do
cu
m
en
t
pr
ep
ar
at
io
n
w
it
h
ea
ch
st
ak
eh
ol
de
r
T
oo
ls
an
d
te
ch
no
lo
gi
es
us
ed
S
ta
ke
ho
ld
er
an
al
ys
is
to
ol
;
on
li
ne
fo
ru
m
;
e-
m
ai
l
O
C
O
P
O
M
O
pl
at
fo
rm
an
d
co
ns
is
te
nt
co
nc
ep
tu
al
de
sc
ri
pt
io
n
(C
C
D
)
V
is
ua
l
m
od
el
s;
da
ta
-fl
ow
di
ag
ra
m
s;
lo
gi
st
ic
s-
fl
ow
di
ag
ra
m
s;
ga
m
es
M
ic
ro
-s
im
ul
at
io
n
m
od
el
in
g
S
im
ul
at
io
n
m
od
el
w.jager@rug.nl

9 Stakeholder Engagement in Policy Development 195
T
ab
le
9.
1
(c
on
ti
nu
ed
) C
as
e
1
C
as
e
2
C
as
e
3
C
as
e
4
C
as
e
5
R
es
ul
ts
/o
ut
co
m
es
of
en
ga
ge
m
en
t
pr
oc
es
s
In
cr
ea
se
d
co
m
m
it
m
en
t
an
d
co
ns
en
su
s
am
on
g
ke
y
st
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vi
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st
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to
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th
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pl
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of
th
os
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st
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re
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lo
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s
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pr
oc
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s
su
pp
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ts
co
ns
en
su
s
am
on
g
st
ak
eh
ol
de
rs
(a
t
le
as
t
in
th
e
sa
m
e
tr
ad
e
la
ne
)
w.jager@rug.nl

196 N. Helbig et al.
9.5 Discussion
In this section, we return to our two guiding questions: What types of engagement
tools and processes are useful for different stakeholders and contexts? And what
factors support the effective use of particular tools and technologies toward con-
structive outcomes? The extant literature reveals a rich history of examining the role
of participation in democratic theory and complex governance (Fung 2006; Fung
et al. 2007). Various analytical tools in the literature address participant selection,
modes of communication, and involvement and many of these were present in the
cases. The cases confirm previous research regarding the importance of stakeholders
and the need for careful and goal-oriented stakeholder selection and engagement.
The cases also demonstrate the importance of support and education for participants
and the role of trusted facilitators, contributing to the knowledge in this field. This
section presents the key findings of our case comparison.
Identifying and Representing Relevant Stakeholders New governance means bring-
ing in stakeholders who are not traditionally part of the policy-making process. Fung
(2006) describes a continuum of types of stakeholders in new governance, including
state representatives (described as expert administrators or elected representatives)
and mini-publics (described as professional and lay stakeholders with organized
interests). Professionals are paid participants (such as lobbyists) or not-for-profit
organizations. Lay stakeholders are those who volunteer their services such as in-
dividuals serving on school councils or neighborhood associations. The cases show
that effective stakeholder engagement requires a nuanced understanding of who are
the relevant stakeholders with respect to the specific goal of the engagement. Each
case represents a complex policy area where the different stakeholders selected or
invited to engage in the policy process represented particular aspects or viewpoints
about a complex problem. Our study confirms that stakeholder analysis helps pol-
icy makers understand differences in stakeholder behavior, intentions, preferences,
interrelations, and interests. It also helps them assess the influence and resources
different stakeholders bring to decision-making or implementation processes (Var-
vasovszky and Brugha 2000). We found that ordinary citizens were seldom involved
in these cases. Despite the common rhetoric of “citizen” participation, the cases show
how it is often impractical to engage members of the public or representatives of the
full range of relevant stakeholders. In these situations, policy modelers and policy
makers needed to appreciate the limitations of stakeholder engagement and aim for
results that take advantage of less-than-complete stakeholder participation.
For example, in the UrbanSim case, only three organizations participated in the
codevelopment of new indicators. The modelers did not treat these stakeholder views
as complete or definitive but rather they used this limited experience to create a
value-based indicator framework to guide further development of new indicators
and future applications of the UrbanSim model. In the international trade case, the
main stakeholder groups were each represented by up to four “exemplary” actors.
In this way, the key positions of these groups were reasonably well represented
in the various activities in the project. These representative actors also served as a
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9 Stakeholder Engagement in Policy Development 197
starting point to identify specific trade lanes where innovations could take place,
and thereby also created awareness of other stakeholders that play a role in those
trade lanes. In the Kosice energy policy case, stakeholder identification was done
using a technique similar to that proposed by Bryson (2004). The local government
was mainly responsible for identifying relevant stakeholders that were invited to the
engagement process. Other complementary techniques such as surveys were used to
assess stakeholders’ roles and expectations. In the international trade case, similar
techniques were applied.
Providing for Participant Support and Education In order to participate in mean-
ingful ways, stakeholders in our cases needed to be educated regarding the purpose
of the engagement, the processes and tools to be used, and the ways in which stake-
holder input would be considered. For all the cases presented, stakeholders, including
those that were often not directly involved in policy making (e.g., citizens, smaller
companies), were made aware of the policy problem in some depth, presented with
opportunities to deliberate the different policy choices, and presented with the in-
formation necessary to understand the expected outcome from implementation of
different policy options.
In the case of EGOV Afghanistan, stakeholders were provided with the results
of an EGOV readiness assessment exercise for them to understand the crucial prob-
lems to be solved through the implementation of a national e-government policy.
Workshops offered them general knowledge about approaches and methodologies
for strategy development. In Kosice, participants were provided with the energy pol-
icy problem and background documents for additional information about the policy
such as the energy conceptions proposed for various cities in the region and studies
of ROI for various combinations of heat energy sources. The descriptive scenarios
and background documents were important for stakeholders to understand the policy
issue, its boundaries, and its challenges. In UrbanSim, the stakeholders were guided
through the process of creating narrative value statements as well as ways to describe
and document indicators in accurate, neutral language. All of these education and
support activities made the stakeholders’ deliberations and input more usable and
more relevant to the problem at hand.
Using Stakeholder Engagement Methods to Reveal and Explain Complex Policy
Problems and Contexts Our cases illustrated that stakeholder engagement is an im-
portant process in policy development as evidenced in the literature reviewed in
Sect. 9.2.3. Engagement helped in all cases to assure that policy processes and pol-
icy decisions were well grounded and responsive to both social values and practical
needs. Action research and living labs helped assure that involvement was not based
on an oversimplified view of the policy problem, Different tools acted as boundary
objects to facilitate knowledge sharing, consensus building, listening, and negotiat-
ing. Models of many kinds were used to break down complex processes and revise
mental models.
In very intractable public problems like trade lanes, in order to understand how
various actors would be affected by different policy options, it was important to un-
derstand how information flowed between actors. The specificity of the models used,
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198 N. Helbig et al.
as well as their comprehensiveness in representing the actual situation, facilitated a
focused debate between businesses and government agencies, forcing each party to
be clear about their precise activities and relevant policy concerns. As a result, no
stakeholder could hide behind a policy that allegedly forced or blocked a certain so-
lution, and the consensus process could focus on the policy options that were feasible
in practice. The Kosice energy policy problem required a balance of diverse interests
of stakeholders both supplying and consuming energy. This presented policy mak-
ers with challenges in identifying and engaging those interests that will affect the
implementation of the new policy. Collaborative scenario building engaged both cat-
egories of stakeholders. This method was particularly important for policy makers to
increase the level of certainty of the policy choice by understanding the intersecting
interests of these stakeholders. Formal policy modeling and simulation were also
important to inform all stakeholders and policy makers of the different possible out-
comes of their scenarios. In the child health case, stakeholders were educated about
the concepts and assumptions underlying the policy-modeling tool being developed.
They also learned from each other about the policy questions of greatest importance
to child health and development. The methods used in these cases are similar to
those identified in literature (Andersen et al. 2007; Vennix et al. 1996) and can be
employed to contribute to many different policy development efforts.
Using Trusted Third Parties to Enhance Transparency of the Process and Improve
the Tools of Engagement Negotiating, brokering, and collaboration skills and exper-
tise with engagement tools are all essential for achieving new forms of governance
(Bingham et al. 2005). The tools and technologies used in our cases have different
characteristics that affect choice and suitability, including available expertise and fi-
nancial resources, level of participation, type of policy problem, and the geographic
location or dispersion of stakeholders. The cases also address a factor that is less
often critically addressed, namely the ways that “trusted” third parties, such as re-
searchers, are used in stakeholder engagement. In these situations, researchers were
not only doing academic research on engagement but also crafting, testing, and im-
proving meaningful tools toward practical outcomes. As “brokers” in the process,
researchers and the tools and technologies they use can inhibit or promote better
models of engagement in policy making and governance.
In the case of EGOV Afghanistan, the use of online surveys by the UNU-IIST
team solved the issue of trying to reach a distributed set of stakeholders separated by
geography and also provided a confidential way to gather information about stake-
holder interests, while the stakeholder analysis tool provided by UNU-IIST helped
MCIT to understand stakeholder preferences and concerns and to assess their po-
tential to influence the policy process. The technology tools used were not intended
to “socialize” the interests of stakeholders but to gather intelligence by a trusted
third party that could be used in the strategic planning process. By comparison, the
intention of the online OCOPOMO platform used in the Kosice case was to bring
the stakeholders themselves into a virtual meeting place where they could see the
interests of other stakeholders. This technology choice, implemented by expert re-
searchers, was intended to facilitate knowledge sharing in a multidirectional way.
w.jager@rug.nl

9 Stakeholder Engagement in Policy Development 199
In the UrbanSim case, the stakeholders’ values and interests were intentionally de-
veloped in isolation from one another because the goal was to represent the distinct
values of each stakeholder type within the model. The simulation mechanism, built
by the academic experts, could then model and report indicators showing how these
different interests might interact over time. In the international trade case, a neutral
party designed the modeling approach and helped the stakeholder groups in each
trade lane model their own existing situations. This approach facilitated joint prob-
lem identification and solution development. In the New Zealand child health case,
researchers helped policy makers discover policy-relevant material while the pol-
icy makers helped the researchers understand what formats and other factors made
that material relevant and usable. Each example demonstrates the role of trusted,
independent experts who can select technology options, tools, and techniques that
introduce transparency into the process and are technically and practically suitable
to the situation. The researchers/modelers were trusted independent brokers who
gathered data, facilitated engagement, and built models or systems to transparently
reflect the reality of the stakeholders.
9.6 Conclusion
All of the cases we reviewed above used an active approach, assisted by third-party
experts, to bring stakeholders together in workshops, through a collaboration plat-
form, or in living labs to support interaction in problem identification, codevelopment
of solutions, and foundations for gaining commitment or consensus by different types
of stakeholders. These experiences go well beyond eliciting stakeholders’ positions
and requirements, leaving the interpretation and balancing to be done by the policy
maker independently. The approaches used in these cases supported the stakeholders
directly in gaining a shared understanding of the problem, providing some insight into
the position and reasoning of other stakeholders, laying the groundwork for potential
negotiation or other ways to find common ground with respect to the policy issue, and
in some cases establishing or reinforcing trust among different stakeholders as well
as trust in the participation process. In line with the literature on this topic, the cases
also illustrate some of the cautions and limitations of stakeholder engagement, with
particular emphasis on the realistic limits of involvement and representation, and the
consequent necessity to match stakeholder selection and engagement methods to a
well-defined goal within the larger policy process.
We find that a careful identification of stakeholders is required, and the selec-
tion depends on the goals of engaging stakeholders. The appropriate selection of
stakeholders to involve can evolve over time, the identification and engagement of
stakeholders is a continuous process, as Bryson (2004) suggests. To illustrate this in
one of the cases, in the international trade case, the process started with a set of stake-
holders needed to identify and initiate the demonstration trade lanes. These provided
grounds for further identifying other stakeholders that play a role in those trade lanes
or that were relevant to the initial set of stakeholders. These needed to be engaged
w.jager@rug.nl

200 N. Helbig et al.
also in order to meet the goals of engaging stakeholders. The goals themselves can
also evolve along the changing stakeholder involvement. In this case, especially in
the beginning, stakeholders were involved to elicit their views and interests in the
matter, whereas during the process this shifted toward engaging stakeholders to en-
sure commitment and to facilitate building consensus among the stakeholders. There
are similarities among the cases such as the use of surveys and convenience sam-
pling as methods to identify stakeholders, face-to-face meetings, and workshops as
methods of engagement and use of modeling techniques as tools and technologies.
Although the literature provides various available methods and techniques used in
stakeholder engagement processes, the cases illustrate that the approaches, tools,
and technologies selected in each case are highly influenced by the purposes and
expected outcomes of the engagement effort. Therefore, we emphasize that every
stakeholder engagement needs to be tailored with well-selected processes and tools
that suit the overall purpose and expected outcomes.
As frequently highlighted in the literature, stakeholders’ involvement in policy
processes can help build consensus by balancing stakeholder interests and pref-
erences, increasing their commitment for policy implementation, and ensuring
transparency and openness of the process. Often, these advantages of stakeholder
engagement are linked to the idea of empowering stakeholders as much as possi-
ble (i.e., stakeholders make key decisions). However, our study shows that all of
these advantages can also be gained by involving stakeholders, with less emphasis
on empowerment. We posit that these benefits can be realized when stakeholders
understand their roles and the objectives of their engagement, enabling them to bring
their own interests to the table while also gaining an understanding of other interests
and factors that influence decisions and results. Therefore, our findings on the im-
portance of offering support and education for participants in order to enable them
to understand their role and the engagement process are an important contribution
to the literature. In a similar vein, the role that trusted (third-party) facilitators could
play in the engagement process is often underestimated in the literature, but is clearly
an important ingredient in the cases presented in this chapter.
Tools can take many different forms, some using technology and some not—the
important factor is to match the tool to the objective and the capabilities of the stake-
holders involved. Making this match requires an understanding of the capabilities
of the stakeholders to use such tools and technologies, sometimes also in a spe-
cific country context. Furthermore, as the UrbanSim and child health case shows,
stakeholders can not only contribute to policy analysis and choices but also make
significant contributions to improving the effectiveness of policy processes, and the
validity and usability of models, and other tools.
Based on these findings, our study offers some practical insights for policy mak-
ers (and researchers) that want to engage stakeholders for policy development. The
first critical step is identification of salient stakeholders or stakeholder types. The
literature reviewed in this chapter as well as the five cases offer various approaches
to identify stakeholders. As concluded above, the method used to identify stake-
holders is closely related to the intended purpose of stakeholder engagement. For
w.jager@rug.nl

9 Stakeholder Engagement in Policy Development 201
example, when aiming to learn from stakeholders about a specific domain, a con-
venience sample of relevant actors is a suitable method. However, if the goal is to
ensure commitment or to build consensus, the methods employed need to be rigor-
ous in identifying all key stakeholder groups. Desk research, surveys, interviews,
and stakeholder or interests mapping tools are useful approaches to do this. Iterative
stakeholder identification often helps create a more complete array of relevant stake-
holders. Our research in combination with the relevant literature also shows other
purposes for stakeholder engagement that guide the selection of stakeholder types.
For example, transparency of the process, facilitating adoption, improving useful-
ness and usability of tools, and enhancing legitimacy are purposes of stakeholder
engagement we found in the cases.
Once the relevant stakeholders have been identified and the objective of involving
them is clear, the approach to stakeholder engagement needs to be selected. Whereas
the literature presents various options, all the cases we covered were in an advanced
stage and almost all employed some form of action research, in which stakeholders
(especially practitioners and policy makers) worked closely with each other and with
researchers in a collaborative way. This was found in all cases, as all cases were fo-
cused on involving stakeholders. In case the objective is to primarily inform or consult
stakeholders, other approaches are more suitable, and some suggestions have been
provided in the background section. When involving stakeholders, policy makers
and researchers will have to carefully consider what role the engaged stakeholders
will have; involving stakeholders to work in real-world complexity as much as pos-
sible will benefit from action research or living labs, but requires that the material,
objectives, activities, etc. be carefully prepared and designed, as stakeholders do not
always have a clear idea of what their involvement should look like or contribute to.
On the other hand, complexity can also be broken down to make the matter more
comprehensible for stakeholders. For this, modeling tools and simulations can be
used for both purposes. In either case, tools and models can function as boundary
objects that stakeholders can view, discuss, or manipulate to better understand how
a particular decision might play out. However, the conceptual capacity stakehold-
ers that will need to have affects the kind and amount of work that should go into
preparing the engagement.
While much remains to be learned about stakeholder engagement in policy mod-
eling, this chapter provides a starting point for better understanding how different
approaches, tools, and technologies can support effective stakeholder participation
toward better policy choices and outcomes. The cases presented here demonstrate
that stakeholder engagement processes, tools, and technologies are versatile and use-
ful to both policy makers and the stakeholders themselves. With careful selection and
application, they can work in a wide variety of situations including different policy
domains and kinds of problems, different political systems, and different levels of
social and economic development.
Acknowledgment This comparison and analysis was conducted as a collaborative activity of
the eGovPoliNet Project, funded through the European Commission Framework 7 Program as
agreement FP7-ICT-2011–288136, and supported by US National Science Foundation (NSF) grant
w.jager@rug.nl

202 N. Helbig et al.
IIS-0540069 to explore policy modeling and governance through an international consortium of
research institutions. Ideas and opinions expressed by the authors do not necessarily represent those
of all eGovPoliNet partners.
We also gratefully acknowledge the information and reference material provided by Peter Davis
and Barry Milne of the COMPASS Center at the University of Auckland regarding the New Zealand
case, and Alan Borning at the University of Washington regarding the UrbanSim case.
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Chapter 10
Values in Computational Models Revalued
The Influence of Designing Computational Models
on Public Decision-Making Processes
Rebecca Moody and Lasse Gerrits
Abstract This chapter aims to add to the technology debate in the sense that it aims
to research the role of values and trust in computational models in the policy process.
Six case studies in which a computational model was used within a complex policy
context were research for the role values play within these models. Conclusions
deal with the role of the designer of the model, the number of different actors, the
amount of trust already present, and the question of agency by humans or technology.
Additionally, margins of error within the model are discussed as well as authority by
one actor over others concerning the model.
10.1 Introduction
Policy makers are tasked with making decisions on issues characterized as wicked
problems because of controversies, unknown relationships between causes and con-
sequences, and (consequently) uncertain futures. From this perspective, it would be
desirable to map the decisions and their possible outcomes prior to the actual deci-
sion making because that would generate certainty in ambiguous situations. Broadly
speaking, this provides the motive for using computational modeling for policy
making as expressed in, e.g., policy informatics. Although there are computational
models that are ready off-the-shelf, it is more common to work with models “mod-
ded off-the-shelf” (MOTS) or even tailor-made models to suit specific questions and
conditions. As such, the model itself becomes part of the decision-making process
during the acquisition.
We observe that this phase, during which scope, functionality, and deployment
are determined by commissioning actors and designers, is essential to the way the
models influence policy making. Although it may be assumed that such models are
neutral or value-free, they are not because of the changes that designer and client
R. Moody (�) · L. Gerrits
Department of Public Administration, Erasmus University Rotterdam,
P.O. Box 1738, 3000 DR Rotterdam, The Netherlands
e-mail:moody@fsw.eur.nl
L. Gerrits
e-mail:gerrits@fsw.eur.nl
© Springer International Publishing Switzerland 2015 205
M. Janssen et al. (eds.), Policy Practice and Digital Science,
Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_10
w.jager@rug.nl

206 R. Moody and L. Gerrits
introduce to the original model. This chapter aims to shed light on the relationships
between computational models and policy making by looking at the role values
play in commissioning, designing, and using such models in policy making. We
will rely on the notions set forward in the technology debate in order to understand
the way technical design can be perceived by actors and used in policy making.
These notions will also be used in our analysis in order to understand the way actors
within the policy process reach conclusions based on the models. We will primarily
look at the perception of the models in terms of values on which we will elaborate
below. We carried out a secondary analysis of case study data we collected for
other research (Gerrits 2008; Moody 2010). The case studies concern: (1) predicting
effects of deepening operations in rivers in Belgium and the Netherlands and (2) in
Germany; (3) determining flood risk prediction in Germany and the Netherlands; (4)
determining the implementation of congestion charging in the UK; (5) predicting and
containing the outbreak of live stock diseases in Germany; (6) predicting particular
matter concentrations in the Netherlands. The chapter is structured as follows. We
will first discuss the theoretical background of our analysis by looking at autonomy
of technology and technology as being deterministic, blending notions from the
technology debate with notions from public administration and public policy in
Sect. 10.2. The methodological approach is discussed in Sect. 10.3, the case studies
in Sect. 10.4, the analysis in Sect. 10.5, and the conclusions in Sect. 10.6.
10.2 Technological Perceptions: The Debate
To understand the implications of the design of computational models it is necessary
to understand the underlying assumptions of the design process. The way modelers
design different models can be viewed from different viewpoints as pointed out in the
technology debate. This is an ongoing debate in philosophy of science as well as in
sociology and technical studies. The technology debate revolves around technology
and humans, technology and society, and technology itself. It reflects on questions
of who drives technology: Are humans the drivers of technology or does technology
drive humans? Does technology possess any values of its own and are these values
given to technology by humans or does technology have no values whatsoever and
is it completely neutral? What is the relationship between technology and society,
does technology constitute society or is it the other way around?
A large number of authors have described the technology debate and placed their
opinion (see: Smith and Marx 1994; Scharff and Dusek 2003; Kaplan 2004). In the
technology debate, several issues are discussed. A central issue is who masters the
other, do humans master over technology, or does technology control humans? An-
other key theme is the question whether technology is autonomous and determines
its own causality. Another key feature is whether technology incorporates values or
should be seen as neutral. Finally, the relationship between technology and society
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10 Values in Computational Models Revalued 207
is important—which drives the other? A number of standpoints within the technol-
ogy debate can be identified. For the sake of briefness, we will only look at social
constructivism and technological determinism and technological instrumentalism.
Within technological determinism, it is believed that technology is not neutral or
value-free. Technology can be good or bad or a mixture of both—this goes for
effects as well as consequences. These consequences may not be dependent on
the desired goal but are dependent on the technology. Technological development,
therefore, does not depend primarily on the intention of the user but is fixed within
the technology itself, it is inevitable and cannot be steered or controlled by humans.
Agency here is not given to the human user but is attributed to technology. It is argued
that certain political and social norms and values are hidden inside the technology.
Therefore, the technology will bring about consequences according to these norms
and values (Ellul 1954, 1990; Zuboff 1988; Heilbroner 1967, 1994; Winner 1977,
1980, 1983, 1993).
In social construction of technology, the viewpoint held is that choices need to be
made in the design and the direction of technology. Economy, society, institutions,
and culture shape the direction and scope of technological development, the form of
technology, the practice, and the outcome of technological change. Agency in this
approach is given back to humans. Technology is neither seen as autonomous nor
does it have a fixed outcome with inevitable consequences. All technology is seen as
a human construct and is thus shaped, or made by humans (Bijker 1993, 1995; Hoff
2000).
What is very important in understanding the approach of social construction of
technology is the technological frame. This technological frame consists of goals,
problems, problem-solving strategies, requirements to be met by problem solutions,
current theories, tacit knowledge, testing procedures, design methods and criteria,
users practice perceived substitution function, and exemplary artifacts (Bijker 1995).
The technological frame is thus the set of rules, ideas, and meanings within a group
and it determines the interaction between the members of a group. This means the
technological frame determines which meaning a group will attribute to a technology
(Bijker 1995).
Within technological instrumentalism, technology is seen as a neutral and value-
free tool. This means a number of things. Firstly, that the technology can be used to
any end. Secondly, this means that technology is indifferent to politics. The technol-
ogy can simply be used in any social or political context since it is not intertwined
with any context. Thirdly, technology is viewed as being rational. It is based on causal
propositions; it can therefore be transferred into any other context as well. Finally,
technology is seen as universal, it stands under the same norm of efficiency in any and
every context (Feenberg 1991). Within the approach of technological instrumental-
ism, technology is not attributed with any agency. This means that technology itself
cannot account for any form of causality; humans cause this causality. Technological
progress, therefore, is viewed as desired progress since it is the human actor who
pursues it (Bekkers et al. 2005). Technology is developed and implemented with the
purpose of achieving one’s goal and the technology serves as a means to achieve this
goal.
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208 R. Moody and L. Gerrits
Authors within all positions agree, however, to the point that in computer technol-
ogy it becomes very difficult to model the real world. Reality is composed of infinite
variables and relationships that pose practical limits to what data can be processed.
Computer models, their designers, and users all are bound in the degree of ratio-
nality they can display. Among others, Simon (1976, 1957), Dror (1968); Lindblom
(1959), and March and Simon (1993) recognized that public decision makers limit
the number of options they consider because of this cognitive limitation. While with
computer models it is assumed by many that this bound in rationality can be lifted,
it can also be argued that this is not necessarily the case (Moody 2010).
10.3 Technology and Public Decision Making
The argument above means that synoptic decision making where the model maps
decision outcomes, to be followed up by the actual decision, its implementation
and possible feedback, is too optimistic an approach. It assumes that a model would
deliver (nonbiased) data, which is judged by decision makers to generate alternatives,
of which the best alternative is chosen and consequently carried out (March and
Simon 1993; Winner 1977; Beniger 1986; Goodhue et al. 1992; Chen 2005). It
is then assumed that a computational model is a value-free tool that will provide
a neutral oversight of all available alternatives with their consequences. Therefore,
it is believed by some that these models will decrease the bounds in rationality
that decision makers face and that public policy making will become a more rational
process in which all consequences are foreseen prior to decision making (Ware 2000;
Moody 2010; Beniger 1986; Goodhue et al. 1992).
This line of reasoning corresponds with the technological instrumentalist view-
point. However, while public decision making is also a political process in practice,
we see that computational models, next to not being able to include all variables
needed for complete consequences, also suffer from limits on the side of political
values. It must be noted that the designer of the model is not a neutral object either
and becomes able to influence the model (Winner 1977; Chen 2005; Ware 2000;
Wright 2008). Known margins of error can be manipulated toward political values
and the necessary choice which needs to be made on which variables to include in
the model is value-driven as well.
The question we need to ask ourselves here is not only whether computational
models are a value-free or neutral tool, but moreover who or what determines the
values within these models. Technological determinists would argue that values are
inherent for the models themselves, and the outcomes of the model are fixed before
use. Social constructivists would argue that the models would be attributed with
value through a process of using the model. We want to take this reasoning a step
further, without taking position in the debate, and look at who designs the model, who
decides which variables should be put into the model, and which variables should be
excluded. Who decides what the functionality of the model is—what is it able to do
and what not—and how do policy makers react to this?
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10 Values in Computational Models Revalued 209
While the above demonstrates that the topic on values in a computational model is
a very loaded and complex topic to begin with, we find that next to the complexity in
the model itself in terms of values, the process of policy making deals with additional
values and its own complicatedness. We have to also address the complexity in the
actual decision-making process because of the multiple actors with diverging norms,
beliefs, and interests. Following the previously stated and driving on outcomes and
hypotheses of previous research (Gerrits 2008; Moody 2010) we find that in dealing
with values in computational models and public policy, some core characteristics
can be identified:
1. The values attributed to the data on which the computational model is based.
These values can be subdivided into:
a. The dominant ideas actors hold on these data, for example, are the data correct,
are they trustworthy? (Trust)
b. The margin of error in the data and how this margin of error is communicated
to policy makers. Are they aware of the correct margin of error, do they under-
stand what this implies, do they feel this is an acceptable margin? (Margins
of error)
2. The values attributed to the model itself, this can be subdivided into:
a. The organization that owns or commissions the model. Is there one organiza-
tion who owns the model, or are there clusters of organizations owning the
model, if so, do they share the same values? (Ownership)
b. Perceptions and values toward the model itself by designers, owners, and other
actors. Do they trust the model, do they feel the outcomes the model produces
are correct? (Beliefs)
3. The values within the decision-making process, this can be subdivided into:
a. Who is the organization which makes the final decision on policy? Are they
codependent on other organizations in order to be able to make the decision
or do they have sole authority? (Authority)
b. Are there other actors involved in the policy making? These actors do not
necessarily need to have the authority to make the decisions but are present
in a policy arena or community affecting the decision or the reception of this
decision. Are there many of such actors? (Multiactors)
In the analysis of the cases, these are the core characteristics to be analyzed.
10.4 Methodology
As mentioned above, we carried out a secondary analysis on original case studies by
us. This was done to change the perspective of our original analysis, which dealt more
with outcomes instead of process. The selected cases share three basic characteristics.
Firstly, they all featured new tailor-made computational models that were deployed
for the first time in the case. Secondly, all cases concern policy issues with the natural
or built environment. Thirdly, all cases concern highly complex and controversial
issues.
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210 R. Moody and L. Gerrits
10.5 Case Studies
The case studies are presented in this section. Each case has a brief introduction. The
main characteristics are presented in Table 10.1. An overview of the stakeholders in
each case can be found in Table 10.2.
Case 1: Morphological Predictions in the Westerschelde (Belgium and the
Netherlands) The Westerschelde estuary runs from the Belgian port of Antwer-
pen through the Netherlands before ending at the North Sea coast. The estuary has
a limited depth and the Antwerpen port authorities were seeking ways to deepen
the main channel in the estuary to facilitate larger ships and thus promote economic
growth. However, the estuary is Dutch territory and the Dutch authorities are reluc-
tant to facilitate the wishes of their Belgian counterparts. They regard the estuary
as a fragile complex system that has a high ecological value and fear that the ecol-
ogy could be destroyed by yet another deepening operation. The estuary consists
of multiple channels through which the tide flows. Those channels are considered
pivotal to the very specific and rare estuarine ecology of which very few remain
across Europe. Negotiations starting in the early 2000s included the extensive use
of computational models to assess the extent to which a deepening would harm the
multichannel morphology of the riverbed and with that the ecological value. Re-
search was jointly commissioned by the Dutch and Belgian authorities. A Dutch
research institute, Deltares (formerly WL-Delft Hydraulics), was the main contrac-
tor, with a small number of subcontractors. It deployed two computational models:
Sobek, which is modified off-the-shelf; and Delft3d, which was a brand-new model
and considered the more advanced but less tested model of the two. Both models
were used to simulate the consequences of dredging operations. The results of Sobek
seemed more robust but were considered relatively crude, while the results of Delft3d
appeared more advanced but featured more model and outcome uncertainty.
Case 2: Morphological Predictions in the Unterelbe (Germany) Like the West-
erschelde, the German Unterelbe is also an estuary that gives access to a major
European seaport. It runs from the port of Hamburg through the federal states
Niedersachsen and Schleswig–Holstein before flowing into the North Sea. Similar
to the first case, the port authorities are seeking for a deepening of the main channel
to facilitate larger ships. Such a deepening was carried out in the 1990s but had
resulted in severe (partly) unforeseen and unwanted changes to the estuary that many
people felt had harmed the ecological state. Here it appears as if the desire to deepen
the estuary had influenced the outcomes of the computational model. The ensuing
societal and political protests, from both nongovernmental organizations (NGOs)
and the federal states except Hamburg itself, led to a different approach when
considering a new deepening in early 2000. The Hamburg port authorities and the
Hamburg Senate commissioned the research to the federal research institute Bunde-
sanstalt für Wasserbau (BAW). This institute collected the relevant data and built its
own model in-house to generate directions for dredging and ecological development.
w.jager@rug.nl

10 Values in Computational Models Revalued 211
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w.jager@rug.nl

212 R. Moody and L. Gerrits
Table 10.2 Overview of relevant stakeholders per case
Case no. Actors involved, including societal stakeholders
1 Bureau Getijdenwateren, Havenbedrijf Antwerpen, Ministerie van Verkeer en
Waterstaat, Office BeNeLux, Provincie Zeeland, Port of Antwerp Expert Team,
ProSes, Rijksinstituut voor Kust en Zee, Rijkswaterstaat Directie Zeeland,
Waterschap Zeeuwse Eilanden, WL Borgerhout, WL Delft Hydraulics, Zeeuwse
Milieufederatie
2 ARGE-Elbe, BUND Hamburg, Bundesanstalt für Stadtentwicklung und Umwelt,
Bundesanstalt für Wasserbau und Schifffahrt, Hamburg Hafen und Logistik AG,
Hamburg Port Authority, Handelskammer Hamburg, Landkreis Stade, NABU
Hamburg, Rettet die Elbe, Senat Hamburg
3 Ministerie van Verkeer en Waterstaat, Rijkswaterstaat, Taskforce Management
Overstromingen, Municipalities Netherlands, Gemeenten Municipalities
Germany, Waterboards, Citizens, Royal Haskoning, Provinces Germany
4 City of London, Transport for London, Citizens, National Alliance against Tolls
5 Friedrich Loeffler Institute, Bundeslanden, Universities, Veterinarians, Citizens
6 Provinces Netherlands, Municipalities Netherlands,Rijks Instituut
Volksgezondheid en Milieu, Landelijk Meetnet Luchtkwaliteit, environmental
organizations
Because of the belief that it should be seen as neutral in the controversial debate,
BAW took the unusual decision to make its data and the model parameters available
to any third party interested to replicate the results or to develop different models.
Consequently, NGOs used the data to model their own particular version of the
dredging works and their consequences. They arrived at different conclusions, which
meant that the commissioning actors were obliged to engage in a dialogue about the
future of the estuary. This slowed down and altered the original plans.
Case 3: Flood-Risk Prediction (Germany and the Netherlands) In the last 2 years,
it was decided to run one application in the Netherlands and Germany with the goal of
predicting and managing floods from rivers. Before this, applications and authorities
were divided on the subject. The application named FLood Information and WArn-
ing System (FLIWAS) was to integrate different applications and organizations to
make sure water management and flood prediction could be done more efficiently.
FLIWAS was developed and the application will predict on the basis of weather con-
ditions, satellite data, past results, and the height of the water whether a flood will
occur and what the damage would be in terms of economics, damage to landscape
and lives. Also, the application is able to calculate proper evacuation routes. The
implementation of the application has resulted in the water sector becoming more
integrated and being able to communicate to policy makers what the result of certain
actions are. It is now more the case than before that water management professionals
are invited to the negotiation table in matters of urban planning, where they are able,
w.jager@rug.nl

10 Values in Computational Models Revalued 213
on the basis of predictions and scenario sketching to convince governments that some
plans might not be wise.
Case 4: Determining the Implementation of Congestion Charging in London
(UK) The city of London has had a large problem with congestion. In order to
find a solution to this congestion problem the local government has come up with a
plan to reduce congestion by imposing a charge on all vehicles that enter the zone in
which the congestion is worst. A computational model was used to determine where
this zone should be so the location of the zone would be most effective in not only
reducing congestion but also gaining the government enough money to reinvest in
public transportation and cycling facilities. On the basis of traffic data, alternative
routes, and public transportation plans the organization Traffic for London had de-
cided on a zone in which the measures are implemented. The application to do so
finds its basis in scenario sketching so different alternatives of the location of the
zone could be viewed with their effects.
Case 5: Predicting and Containing the Outbreak of Livestock Diseases
(Germany) Due to European regulations and after the outbreak of mouth and foot
disease in the 1990s which caused significant financial damage, the German govern-
ment decided to centralize all information on contagious livestock diseases into one
application, TSN (TierSeuchenNachrichten). The application holds information on
farms and animals. Further, the application will make scenarios on how to contain
and prevent outbreaks of contagious diseases. On the basis of the contagiousness
of the disease, the estimated health of animals, natural borders, wind and weather
conditions, and the location of farms, a decision can be taken on what measures to
take. These measures include the killing of the animals, vaccination of the animals,
or installing a buffer zone in which no traffic is allowed. The German government
appointed the Friedrich Loeffler Institute with the task to develop and manage the
application.
Case 6: Predicting Particular Matter Concentrations (the Netherlands) Particulate
matter in recent years has become an issue more and more prone to attention. Due
to European regulations, the countries in the EU are to make sure the concentration
of particulate matter in the air does not exceed a set norm. Therefore, whether
buildings and roads can be built becomes dependent on this norm, not only for
the effect on air quality by the building process but also for the effect of the plans
once in use. Applications have been made to predict the potential concentrations
of particulate matter after implementation of building plans, the outcome of the
prediction determines whether a building can be built. The problem in this case lies in
the fact that the way to calculate particulate matter to begin with is unclear, scientists
are not sure of the calculation as of now, the health effects are not clear as well, just
like the prediction itself. Furthermore, other NGOs have made their own application
to predict concentrations, in which mostly the outcome differs significantly from
the applications local governments use. This causes each building process to be
reevaluated for their legitimacy, and this causes a lot of distrust.
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214 R. Moody and L. Gerrits
10.6 Analysis
When analyzing our empirical data on the basis of the core characteristics earlier
established we find a number of things. These will be discussed below.
Values in Data We have distinguished in trust and margin of error and there are
some trends to be discovered in our six cases. First, we find that the trust and the
reliance on the data in the model are large in the cases 1–4 and low in the cases 5 and
6. In order to explain this we must realize that this in fact has no causal relation to the
data itself but to the cases in specific and the actors involved. What we find is that in
cases 1–4 the actors involved in the cases share a common goal and common values,
in the cases 5 and 6 there are several groups of actors with different goals. The trust
an actor has in the data can, therefore, be explained by the trust and common values
and goals he has with other actors. When there are different goals among actors or
groups of actors, we find that the trust in the data itself in the model decreases. When
relating this back to our theoretical notions we find that a determinist viewpoint
would be difficult to hold since the trust in the data does not depend on the manner of
collection of data or on the model itself but on the diversity of the goals and values
of actors involved. It must be noted however, that each of the actors, both in the
cases in which there is trust in the data as well as those cases without trust in the
data themselves do hold a deterministic viewpoint. They feel that the data will lead
to better solutions in cases 1–4 and in cases 5–6 they believe the data will only lead
to a politically motivated outcome serving another actor.
When looking at the margins of error we find that the margins of error are relatively
high in all cases. This can be explained by the large number of variables within the
cases and their complex interrelation. In all cases, those actors involved acknowledge
these errors but also realize that politicians want to hear a nominal “yes” or “no”
answer. Therefore, these margins of errors disappear in the communication between
the experts and policy makers as the experts simplify the presentation of their results.
While the trust in the data and the reliance on the model is high in most of our cases,
we still find a high margin of error which is only acknowledged by actors in cases in
which the trust in the data is low. This shows us that the objective margin of error will
only be perceived as a “problem” or an “issue that needs to be taken into account,”
when there is little trust in data. Not only are these margins emphasized but also on
the basis of these margins actors accuse each other of manipulation of the model and
the data for their own political goal. Taking this into account it can be concluded that
for both the trust in the data and the margins of error, the group of actors and their
goals, are determinant for the course of the process. When actors agree on goals the
trust in the data is high and margins of error are neither communicated, nor seen as
a problem. When actors do not agree on the political goal, trust is low, the margins
of error are emphasized, and the manipulability of the data is communicated very
frequently.
Values in the Model When we look at the values in the model itself, we have
distinguished between ownership and beliefs. In terms of ownership, it can be found
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10 Values in Computational Models Revalued 215
that most models, except for the case 6 are owned and developed by one organization.
Therefore, it is the case that they have a monopoly on the information generated by
the model, which grants them the power to use this monopoly in terms of decision
making. Only in cases 2 and 6 we see that other actors use the data and the information
to build their own model. In both cases, this has led to conflict. We also find that in
case 5 some issues of ownership have occurred, but these issues were only addressed
by actors not sharing the same political goal as the actors who owned the application,
they were not granted access, while other actors who did agree with the political goals
of the owner were granted access. What this leads us to conclude is that ownership
by actors who share different political goals will lead to a conflictuous process of
policy making. The manner of ownership or monopolization of a model by a (group
of) actor(s) with the same political goal, therefore, does influence the outcome of the
policy-making process as well as the process itself.
In terms of beliefs or trust in the model, we find the same results as for trust in the
data, which would appear logical. The explanation of why some models are trusted
and others are not, is the same explanation as for the trust in the data. In cases with
actors with different goals, the trust in the model is generally low, the situation is
conflictuous, and those opposed do not trust the model and accuse the owner of the
model of distrustfulness, using the application for their own political, motivation,
and manipulation of the model so their preferred outcome will prevail. Here as well
we find that actors individually hold a fairly deterministic viewpoint regarding the
model in question. Additionally, it shows us that data in the model and the model
itself cannot be seen separate from each other in terms of trust.
Values in the Decision-Making Process When we look at the values of the decision-
making process we have distinguished between authority and multiactor setting. We
can find that in terms of authority an interesting situation exists. In some cases, cases
1, 4, and 5, there is a clear line of authority. In these cases, it is agreed upon who
should provide the data, the model, and the results on the basis of which policy
should be made. In most cases, this is institutionally arranged, by legally making
one organization responsible. In some cases, this is arranged by a code of conduct
in which all agree this to be the organization dealing with this topic. In the cases
2, 3, and 6, we find that there is no clear agreement on who holds authority. This
can be explained by the idea that more than one organization is using the same data
but reaches different policy conclusions based on this data which eroded authority
(cases 2 and 6) or by monopolization issues, in which one organization used to hold
authority over policy decisions but because of the emergence of the model and the
monopolization of this information authority has become blurred (case 3). The lack
of clear lines of authority accounts for a situation of conflict, different actors are
trying to use the outcomes of the models for their own political goals. A very social
constructivist situation in which technological frames of actors create a situation in
which they believe the outcome of the model supports their claims, policy solutions,
and goals.
A final factor is the number of actors and their relation with one another. Natu-
rally, a number of different interests can be found in each case and a clear trend on
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216 R. Moody and L. Gerrits
this variable is not to be found, it seems to be rather case specific. In case 1, we see
that there is a high number of actors involved in the decision-making process and
that the diversity of the actors regarding their political goals and convictions is also
high. This complicates the decision-making process. Case 2 shows that the number
of actors involved is somewhat low in the first case but the main authorities share
the same convictions, which ostensibly simplifies the decision-making process. The
fact that opponents have organized themselves efficiently and have had access to the
same data but with different results means that in the end the decision making was
as tiresome as in the first case. Cases 3 and 5 provide us with insight on how a group
of actors can become very powerful in the decision-making process because they
have the monopoly on the information. In case 4, it becomes clear that institutional
arrangements can reduce complexity since only one organization has formal author-
ity. Finally, case 6 tells us that the lack of trust, the enormous difference in political
opinion, and the lack of one owner and authority make decision making so complex
that a decision that is seen as legitimate by all actors becomes impossible. In general,
taking the previous part of the analysis into account it shows that the actual number
of different actors has no influence, it is the number of different goals they hold.
10.7 Conclusions
This chapter aims to answer the question how the designing and using models and the
communication between designer and policy maker influences the process of public
decision making in terms of values. Analysis of the six cases shows that this influence
is considerable. We find that a large diversity exists within the cases on the different
values we have evaluated and that the impact of these values, perceptions, and beliefs
is very important for the process of policy making. This is because when actors think
and believe the same things, they tend to think that their work encompasses all possi-
ble variety. In other words, being of the same mindset triggers unintentional selective
blindness. Consequently, the models are not under close scrutiny and decisions made
using a certain model reflect the biases that were unintentionally programmed into
the model. For example, in the case of predicting the outbreak of livestock diseases,
it appeared that the option “clearing of animals” could never be a feasible outcome
of the model, whereas in reality it could be a possible answer.
A high diversity in actors raises a situation of conflict as multiple actors bring
forward their own perspectives that are in many cases only partly convergent and
downright contradictory in some cases. In other words, higher diversity leads to
more obvious clashes of goals, beliefs, and values. The models that are used and
the results that the models generate are being questioned more explicitly and openly,
consequently leading to a higher perceived complexity as it becomes much more dif-
ficult to reach a quick conclusion. Diversity or lack, thereof, is partly a design feature
of the institutional dimension, partly an unintentional process between actors who
trust and believe each other. As a design feature it emerges when the commissioning,
developing, and using models are clustered around one or a limited set of tightly
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10 Values in Computational Models Revalued 217
coupled actors. Such a concentration of power, where both the research and the final
decision are strongly linked, causes the actors to develop a bias towards their own
ideas. Whether this link is institutionally determined or not, does not influence this.
The models are consequently used as such. When such close links are contested or
absent, the diversity raises because of the possibility of questioning current ideas and
beliefs. As an unintentional process, it emerges when actors develop relationships of
trust and belief. Although actors are not aware of it, such relationships still promote
convergence of thinking, thus decreasing contradictory ideas.
If anything, the current research shows that models and data never speak for
itself. On the contrary, they are heavily influenced by the social dynamics of the
context they are developed in. Not only, as social constructivists claim, because of
the values attributed to the models while they were used in their own political and
institutional context but also as the technological determinists argue, because of the
values in the model itself. They have been put there by the designers’ choice, often
unintentionally; however, public policy makers are unaware of these choices. This
leads us to conclude that computational models have a very large influence on the
decisions that are made, as our case study shows. Following this we can argue that
the potential power of these models within public decision making is substantial.
Even though throughout this chapter we have argued that humans do have agency
over technology our case studies show that this agency at some points is limited
to the designer of the application and not to the public decision maker using the
application in order to come to a decision. This raises questions for the future in
which we ask ourselves that when computational models are normative because they
cannot mimic full reality and instead reflect the developers’ and users’ ideas, what this
means for those elected officials using the computational model for decision making.
We observed often that belief in the model as the right descriptor and predictor of
reality was almost absolute at the level of policy makers. “If it has a number it must
be true.” We argue that this number is as much a reflection of the developers’ ideas
as it reflects reality.
Furthermore, we can conclude that not only the designer of the model is able to
place values into the model but these values are also incorporated by the users of
the application. Not because through a technological frame, they attribute a certain
meaning to a technology but because the data in the model itself is not flawless. This
means that is possible that models used by different actors generate entirely different
outcomes. It is not as much a design flaw of the model but rather a consequence of
the complexity of values of data and models. However, public decision makers are
often unaware of this and regard the model as being neutral and value-free. Designers
often in their communication with public decision makers are trying to simplify their
message and are trying to hide the normative biases.
Concluding, we can state that while the technology debate remains an ongoing
debate, in terms of computational models and public decision making it is also
important to research the relationship between the designer of the model and the
public policy maker, the role of the designer and its interaction with end users and
policy makers should be further researched. The nature of this relationship accounts
partly for a more deterministic or more social constructivist view on the side of the
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218 R. Moody and L. Gerrits
public policy maker regarding technology. Therefore, this chapter cannot conclude
whether technology should be viewed in either a social constructivist manner or a
deterministic manner, but can conclude that different actors view the same technology
in different epistemological manners.
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w.jager@rug.nl

Chapter 11
The Psychological Drivers of Bureaucracy:
Protecting the Societal Goals of an Organization
Tjeerd C. Andringa
Bureaucracy is the art of making the possible impossible
–Javier Pascual Salcedo
A democracy which makes or even effectively prepares for
modern, scientific war must necessarily cease to be democratic.
No country can be really well prepared for modern war unless it
is governed by a tyrant, at the head of a highly trained and
perfectly obedient bureaucracy.
–Aldous Huxley
Whether the mask is labeled fascism, democracy, or dictatorship
of the proletariat, our great adversary remains the
apparatus—the bureaucracy, the police, the military. Not the
one facing us across the frontier of the battle lines, which is not
so much our enemy as our brothers’ enemy, but the one that calls
itself our protector and makes us its slaves. No matter what the
circumstances, the worst betrayal will always be to subordinate
ourselves to this apparatus and to trample underfoot, in its
service, all human values in ourselves and in others.
–Simone Weil
Abstract This chapter addresses the psychological enablers of bureaucracy and
ways to protect bureaucrats and society from its adverse effects. All organizations
benefit from formalization, but a bureaucracy is defined by the dominance of coer-
cive formalization. Since bureaucrats are not bureaucratic among friends, one might
ask what changes someone at work into a bureaucrat and why do bureaucrats and
bureaucratic organizations exhibit their characteristic behaviors?
The pattern of behavior arises from fundamental psychology and in particular
(1) our capacity for habitual behavior, (2) the difference between intelligence as
manifestation of the coping mode of cognition and understanding as manifestation
of the pervasive optimization mode, and (3) the phenomenon of authoritarianism
as the need for external authority through a lack of understanding of one’s living
environment. The combination of these phenomena leads to a formal definition, the
“Bureaucratic Dynamic,” in which the prevalence of coercive formalization scales
T. C. Andringa (�)
University College Groningen, Institute of Artificial Intelligence and Cognitive Engineering
(ALICE), University of Groningen, Broerstraat 5, 9700 AB, Groningen, the Netherlands
e-mail: t.c.andringa@rug.nl
© Springer International Publishing Switzerland 2015 221
M. Janssen et al. (eds.), Policy Practice and Digital Science,
Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_11
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222 T. C. Andringa
with “institutional ignorance” (as measure of how well workers understand the con-
sequence of their own (in)actions, both within the organization as well on the wider
society) and “worker cost of failure.”
Modern organizational theory has become progressively more aware of the inef-
ficiencies and dangers of bureaucracy. The framework developed in this paper can
be applied to protect society, organizations, and workers from the adverse effects of
bureaucracy. Yet while non-bureaucratic organizations can produce excellence, they
also rely on it and are therefore somewhat fragile. Improved protective measures can
be developed using the framework developed in this chapter.
11.1 Introduction
In 2005 a Dutch insurance company aired a television commercial1 in which they
showed a mother and daughter trying to collect their “purple crocodile” at a lost-
and-found department. The clerk reaches for the missing object form—just next to
the huge purple crocodile—and hands it to the mother to be filled in. After a few
attempts the form is filled-in to the clerk’s satisfaction and he instructs the family to
collect the missing object the next morning between 9 and 10 a.m. “But it’s there”
the mother remarks. “Yes it is there” the clerk responds with an empty expression to
this completely irrelevant remark.
Clearly, the original societal role of this lost-and-found department was replaced
by a new goal: procedural correctness, irrespective of the state of the world and the
implications of following procedure. The commercial ended with the remark that
less bureaucracy is preferable.
We all know these blatant examples of bureaucracy, where form and proce-
dure have become stultifying, any genuine empathy and human decency is absent,
and the organization is no longer serving its original purpose efficiently. Yet the
most shocking, albeit not normally acknowledged, aspect of these examples is that
bureaucrats—outside the direct working environment—are just regular law-abiding
individuals who might do volunteer work and who will gladly return something
without insisting on a form to fill in first: among friends no-one is a bureaucrat.
I consider bureaucracy and bureaucratic mindsets as suboptimal or even patho-
logical for the organization because it has adopted self-serving goals in favor of its
original societal goal and for the bureaucrat because he or she is reduced—at work—
to a shadow of his or her full human potential. This paper addresses the psychological
reasoning on which this opinion is based.
Administration is not necessarily bureaucratic. And formalization—the extent
of written rules, procedures, and instructions—can both help and hinder the overall
functioning of the organization. In this chapter, I define bureaucracy as the dominance
of coercive formalization within professional organizations. Coercive formalization
1 https://www.youtube.com/watch?v = 2Rw27vcTHRw
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11 The Psychological Drivers of Bureaucracy 223
takes away autonomy and changes a worker into the direction of an automaton:
someone who can be easily replaced by information technology or a robot.
All human activities benefit from some form of formalization. Formalization
allows automating routine tasks, to agree on how to collaborate, determine when
and how tasks should be executed, and when they are finished. As such, procedures
should not be changed too often so that they become and remain a stable basis
for organizational functioning. Yet procedures should also not be too static and too
strictly adhered to so that they lead to stultification, suboptimal task execution, and,
above all, to loosing track of the societal goals of an organization. These are all signs
of bureaucracy.
The bulk of this chapter comprises the formulation of a psychological framework
that explains the phenomenology of bureaucratic and non-bureaucratic organizations.
This framework is based on the two cognitive modes—the coping mode and the per-
vasive optimization mode—that we defined in an earlier paper on Learning Autonomy
(Andringa et al. 2013). Since bureaucracy has a lot to do with preventing worker au-
tonomy, it is not surprising that our paper contains relevant ideas. What I found quite
surprising, and highly relevant, was how well the “coping mode of cognition” fitted
with the bureaucracy literature (Adler and Borys 1996; Weber 1978). In Learning
Autonomy we had addressed the phenomenon of authoritarianism: the need for and
acceptance of centralized or group authority. In this chapter I show that bureaucracy
is a manifestation of authoritarianism in the context of professional organizations.
Based on the defining characteristics of authoritarianism, I predict the incentives for
coercive formalization, and with that the incentive for bureaucracy, as follows:
Incentive for coercive formalization = Institutional ignorance × Worker cost of failure
I call this the Bureaucratic Dynamic. Maximizing “institutional ignorance” and
“Worker cost of failure” leads, via psychological mechanisms outlined below, in-
evitably to more bureaucracy. Fortunately, minimizing these will reduce bureaucracy.
I predict that this formula can be used as an effective means to improve our under-
standing of the phenomenon, to improve effective anti-bureaucracy measures, and
to expose ineffective ones.
This chapter provides a transdisciplinary approach of bureaucracy. Transdisci-
plinarity entails that I will ignore traditional (and often quite arbitrary) disciplinary
boundaries and I will address multiple description levels; in particular a number of
subdisciplines of psychology (fundamental science level), organizational research
(applied science level), policy (normative level), and ethical considerations (value
level) (Max-Neef 2005).
I start in Sect. 11.1, with an interdisciplinary analysis addressing how the diversity
of bureaucracy can be understood through the degree and the type of coercive and
enabling formalization. This analysis outlines many manifestations of bureaucracy
that, together with the observation that no one is a bureaucrat among friends, demand
a psychological explanation.
Section 11.2, forms the fundamental science bulk of this chapter. In it, I start
with habits as effective and goal realizing activities that require only a minimal
involvement of the higher faculties of mind because the behavior originates from
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224 T. C. Andringa
and is guided by the (work) environment. This is followed by the observation that
the two modes of thought we have defined in our earlier paper on Learning Autonomy
(Andringa et al. 2013) match bureaucratic and non-bureaucratic strategies. The two
modes differ in the locus of authority: external for bureaucracy and internalized
for non-bureaucratic approaches. The centrality of the concept of authority becomes
even clearer when I change the perspective to political psychology and in particular to
the opposition authoritarians–libertarians. These groups of people differ in whether
or not they (unconsciously) consider the complexity of the world too high to act
adequately and feel comfortable. The shared feelings of inadequacy motivate them
to instill order through coercive formalization and group or centralized authority: a
phenomenon known as the “Authoritarian Dynamic.” This dynamic, in this chapter,
applied in the context of professional organizations, drives the growth or demise of
bureaucracy according to the “Bureaucratic Dynamic.” Section 11.2 closes with a
short reflection on the (serious) detrimental effects of bureaucracy might have on
bureaucrats (value level).
This chapter closes with a shorter section on how three modern management
paradigms (applied science and policy level) can be classified according to the
prevalence of the coping or the pervasive optimization mode. This entails, in some
sense, that experiential evidence has already discovered what I argue from a psy-
chologically informed perspective. Yet this perspective complements and enriches
the experientially acquired understanding. I then direct attention to non-bureaucratic
or “libertarian” organizations. One crucial aspect of these is that they not only are
able to deliver pervasive optimization of all organizational roles, they also depend on
it. This entails that they are fragile and easily wrecked by workers with insufficient
institutional understanding. I give examples of how this degradation process typi-
cally occurs and indicate a number of “red flags.” I end the chapter with a number
of conclusions and observations.
11.2 Characteristics of Bureaucracy
This section is based on the analysis of organizations with different types, levels
and forms of bureaucracy by Adler and Borys (1996). They provide an insightful and
fairly comprehensive analysis of bureaucracy and its diverse forms. In addition Adler
and Borys propose a structured typology of organizations that matches very well with
our recent paper on open-ended (lifespan) development and in particularly with the
development of bounded or full autonomy (Andringa et al. 2013). Taken together,
these two articles provide an interesting generalized perspective on bureaucracy
and, in general, on some foundational perspectives on human autonomy and human
organizations.
Adler and Borys address the issue of worker autonomy in many different examples
and remark “that much of the literature on the sociology of scientists and engineers
asserts that employees in these occupations typically aspire to high levels of auton-
omy in their work and that bureaucratic formalization undermines their commitment
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11 The Psychological Drivers of Bureaucracy 225
and innovation effectiveness.” Yet other employees might benefit from bureaucratic
formalization. Consequently:
Organizational research presents two conflicting views of the human attitudinal or outcomes
of bureaucracy. According to the negative view, the bureaucratic form of organization stifles
creativity, fosters dissatisfaction, and demotivates employees. According to the positive
view, it provides needed guidance and clarifies responsibilities, thereby easing role stress
and helping individuals be and feel more effective.
In terms of autonomy, it seems that the bureaucratic form of organization stultifies the
functioning of highly autonomous and motivated employees, while it actually pro-
vides the less autonomous employees guidance and effectiveness in roles in which
they would otherwise not be able to function. So bureaucracy constrains the au-
tonomous employees, but enables the less autonomous to contribute more effectively.
Accordingly, Adler and Borys conclude that the study “of the functions and effects
of bureaucracy has split correspondingly with one branch focused on its power to
enforce compliance from employees assumed to be recalcitrant or irresponsible and
the other branch focused on bureaucracy’s technical efficiency.”
Based on this observation and a number of examples, Adler and Borys propose
two structural dimensions for organizations: the type of formalization, spanning a
continuum from coercive to enabling, and the degree of formalization from low to
high. This leads to a two-dimensional representation with four quadrants resulting
from the intersection of the axes as depicted in Fig. 11.1. The degree of bureaucracy
is represented by the diagonal connecting a high degree of coercive formalization—
characteristic of a highly bureaucratic or “mechanistic” organization—to a low
degree of enabling formalization in the non-bureaucratic, or “organic,” organization.
The other diagonal corresponds to a highly centralized, or “autocratic,” organization
or a decentralized “enabling bureaucracy.”
The key component of this organizational typology is formalization and Adler
and Borys describe many different aspects of formalization. A number of these are
summarized in Table 11.1.
It will be clear from Table 11.1 that some degree of suitable formalization is
highly beneficial, and probably defining for any organization and as such is broadly
supported. Yet, too much formalization or formalization of an unsuitable kind will
be detrimental for the employees and the way the organization realizes its societal
mission and as such enacts its raison d’être.
Adler and Borys couple the two types of formalization—coercive and enabling—
to perspectives on the organization. The “enabling approach” considers workers as
sources of skill and intelligence to be activated. This works, of course, for workers
who enjoy to be challenged, who aspire to develop their skills, and who feel a personal
or shared pride regarding the work they are performing. In the “coercive approach”
workers are treated as sources of problems to be eliminated. In this approach the
opportunism and autonomy of workers (skilled or not) is to be feared and it leads
almost inevitably to a deskilling approach. Deskilling is, of course, resented by those
who consider work autonomy and skill-development essential for personal growth,
but for the less skilled and probably more insecure workers, who know they will not be
able to contribute effectively without strict and firm guidance, the coercive approach
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226 T. C. Andringa
Fig. 11.1 Types of organization. (Based on Fig. 1 in Adler and Borys (1996))
is a way to contribute on a higher professional level than they would otherwise be
able to achieve. Adler and Borys provide many properties of coercive and enabling
formalization, which are summarized in Table 11.2.
As Table 11.2 shows, the basic logic of the coercive approach is to curtail the scope
of behavioral options of workers through centralized and/or (corrective) group au-
thority. In contrast, the basic logic of the enabling approach is to use diversity of
insights and independent judgment of all employees to improve all aspects of the orga-
nization (in the context of all its roles and obligations). As such the enabling approach
relies on a combination of group authority and individual authority. But note that the
role of group authority differs between the two approaches: in the coercive approach
it is to signal and correct any deviant behavior, while in the enabling approach it is
a means to aggregate organizational understanding in a common mode of working.
Asymmetries in power, of course, promote the coercive approach, but the same
holds for ignoring or actively suppressing the skills and knowledge of the workers
since this almost inevitably impoverishes the understanding of the organization and
as such it leads to organizations that progressively become out-of-sync with reality:
instead the organization creates its own peculiar realities based on whatever pleases
the power structure, which progressively makes it more difficult to apply the ob-
servations, knowledge, and insights of the workers for the proper execution of the
organization’s societal role.
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11 The Psychological Drivers of Bureaucracy 227
Table 11.1 Positive and negative aspects of formalization. (Based on Adler and Borys (1996))
Negative effects of formalization: Positive effects of formalization:
Higher absences Formalization can increase efficiency
Propensity to leave organization Embrace of well-designed procedure facilitates task
Physical and psychological stress Performance and pride on workmanship
Reduced innovation Reduction of role conflict and role ambiguity
Reduced job satisfaction Increased work satisfaction
Reduced commitment to the organization Reduction of feelings of alienation and stress
Can help innovation if it capture lessons of prior
Lower motivation Experience or help coordination of larger-scale projects
broad preference and benefits for routine tasks
Formalization is disfavored if: Formalization is favored if:
Rules benefit managers: especially when
rules are also used to sanction
Work is considered as a cooperative endeavor rather than
the abrogation of autonomy
Bad rules/procedures: Good rules/procedures:
Resented Taken for granted
If possible ignored or avoided Hardly noticed
Adler and Borys couple the motivations (Deci and Ryan 1987) to participate in
the organization to the type of formalization. The coercive formalization corresponds
to external (authority enforced, fear of punishment, rule compliance) or introjected
motivation (internal or esteem-based pressures to avoid harm) because it does not
tap into whatever is intrinsically motivating for the employees. The enabling for-
malization does just that: it allows motivation based on identification with personal
importance or compliance with personal goals. It might even allow intrinsic moti-
vation in the form of completely unconstrained and self-determined activities that
involve highly enjoyable states like flow and play.
These motivations—in this order—have been coupled to the perceived locus of
causality (PLOC), which reflects the degree the individual or some external author-
ity or influence originates the behavior (Ryan and Connell 1989). It is a measure of
autonomy and agency. The more autonomous the behavior, the more it is endorsed
by the whole self and is experienced as action for which one is responsible (Deci and
Ryan 1987). In particular for activities with an external PLOC individuals do not re-
ally feel a personal responsibility and probably no moral responsibility as well. This
then suggests that it is possible to realize highly unethical goals by promoting the
coercive form of formalization: the workers will not feel any sense of responsibility.
This explains why bureaucracies (or more general hierarchical organizations subject
to coercive formalization, such as the military, intelligence agencies, or some multi-
nationals) are so often involved in atrocities. Aldous Huxley’s quote at the beginning
of this chapter acknowledges this as well.
w.jager@rug.nl

228 T. C. Andringa
Table 11.2 Properties of coercive and enabling formalization
Coercive formalization Enabling formalization
Basic attitude Basic attitude
Workers as sources of problems to be eliminated.
Opportunism of workers to be feared: deskilling
approach
Workers as a source of skill and intelligence to
be activated
Key properties: Key properties:
The formal system (e.g., organogram) is leading,
workers exist to serve their role
The formal system exists to enable and support
the workers in executing the societal function of
the organization
Deviation from the protocol is suspect Deviations from procedure decided by the work-
ers
Procedures often non-transparent to keep knowl-
edge about the organization from the employees
to prevent “creative interaction”
Deviations from the protocol signals the need for
better procedures or methods and are a learning
opportunity
Procedures as assertions of duties (not to help) Procedures help to explain key components and
codifying best practices
“Global transparency” highly asymmetric, with
procedures that, for example, help to real-
ize a panopticon (so that employees know that
superiors can monitor them at any time)
Procedures to provide insight into personal per-
formance
Global transparency of the organization is a
source of employee initiative and as such a risk
to be minimized
Global transparency provides insight in the role
of processes in the broader context of the orga-
nization as necessary source of innovation and
improvement for the whole organization
Procedures define, in detail, a sequence of steps
to be followed and force the employee to ask
approval for any deviation of the protocol (such
as skipping unnecessary steps)
Forces promoting the coercive formalization: Forces promoting the enabling formalization:
Asymmetries in power Societal preference for enabling formalization
Absence of reality checks associated with an in-
ward focus in which local conflicts become more
important than organizational goals
A necessity of a very complex task environment
(such as in times of competitive pressure)
The results of automation (whatever ICT pro-
duces) needs to be communicated and followed-
up to the letter
Automation first replaces routine operations
(their formalization become part of the ICT) and
leads to a demand for more skilled employees
Motivation type: Motivation type:
External (authority enforced, fear of punish-
ment, rule compliance)
Intrinsic motivation (completely
self-determined activities)
Introjected motivation (internal or esteem-based
pressures to avoid harm)
Identified (with personal importance) or inte-
grated (compliance with personal goals)
w.jager@rug.nl

11 The Psychological Drivers of Bureaucracy 229
In this section I have outlined a number of properties of bureaucracies for which
I will propose the psychological underpinnings in Sect. 11.3. This section will focus
on why the phenomena outlined before, emerge inevitably from basic psychology.
11.3 Psychological Roots of Bureaucracy
11.3.1 Habits
Since the formalization, and therefore automation, of behavior is an integral part
of bureaucracy it makes sense to address the topic of habits and habitual behavior
because the psychological term “habit” refers to an automatic response to a specific
situation (Ouellette and Wood 2003; Wood and Neal 2009). The ability to behave ha-
bitually is a wonderful thing, because it means that we have learned to do something so
efficiently that our minds are kept free for other things. Habits can be nested so that for
example, the habit of driving can be part of daily, weekly, monthly, or yearly routines.
Habits are well-trained perception–action relations that are efficiently combined
so that they address our daily affairs with minimal mental effort and their combination
may lead to an endless variety of effective, while still seemingly effortless, behaviors.
During the execution of a habit it is the environment that determines your actions:
if there is a door on your path, you open it. You will not normally initiate the door
opening behavior without a door. You can of course, willingly, try to activate door-
opening behavior in the middle of a lawn. But nothing in the lawn-environment will
activate this particular behavior. This holds for steak cutting, hair combing, wall
painting, and turning the page of a newspaper: you can do it whenever you want,
but it is only productive (and looks less silly) if you let the environment activate the
desired behavior. That is the reason why each habit is activated in situations that
provide the affordances to activate the behavior.
The way we respond to social or work situations is also for a large part habitual.
In particular we find “that mental content activated in the course of perceiving one’s
social environment automatically creates behavioral tendencies” (Bargh 2010). The
first time we encounter some situation we might not know what to do and to give it
all our attention to decide on appropriate behavior, but after a few times practice, the
situation is neither novel nor challenging and we respond habitually and according to,
for example, the stereotypes activated by the environment. Because of the flexibility
of habitual components and because of the minimal mental effort it costs to combine
them adaptively, most of our daily activities are habitual, which is good because
during habit execution we are left with ample opportunities to direct our attention to
interesting, useful, or important things.
William James, one of the first and still one of the greatest psychologists, had much
to say on habits. In fact he addresses the topic of habits as one of the foundations
of psychology. And what is relevant for this chapter, he explicitly defined habit, 125
years ago, as the flywheel that keeps society (and the organizations that constitute
it) stable (James 1890, p 16–17).
Habit is thus the enormous flywheel of society, its most precious conservative agent. It alone
is what keeps us all within the bounds of ordinance, and saves the children of fortune from the
w.jager@rug.nl

230 T. C. Andringa
envious uprisings of the poor. It alone prevents the hardest and the most repulsive walks of
life from being deserted by those who are brought up to tread therein. It keeps the fisherman
and the deckhand at sea through the winter; it holds the miner in its darkness, and nails the
countryman to its log-cabin and its lonely farm through all the months of snow; it protects
us from invasion by the natives of the desert and the frozen zone. It dooms us all to fight out
the battle of life upon the lines of our nurture or our early choice, and to make the best of a
pursuit that disagrees, because there is no other for which we are fitted, and it is too late to
begin again.
So habits do not only free our minds for more important things they also keep us
within the bounds of the status quo or pursuits once started. Habits are not a genetic
inevitability, but are the result of the way we are raised, educated, and introduced
in our professional lives. James defines the role of education therefore in terms of
acquiring habits.
The great thing, then, in all education, is to make our nervous system our ally instead of
our enemy. It is to fund and capitalize our acquisitions, and live at ease upon the interest
of the fund. For this we must make automatic and habitual, as early as possible, as many
useful actions as we can, and guard against the growing into ways that are likely to be
disadvantageous to us, as we should guard against the plague. The more of the details of
our daily life we can hand over to the effortless custody of automatism, the more our higher
powers of mind will be set free for their own proper work.
The original text had an emphasis in italic, here I have added an emphasis in bold to
focus on the fact that habits may not necessarily be beneficial to us, they might in
fact be more beneficial to whoever has defined the status quo and now benefits from
the habitual continuation of that status quo. This status quo can typically be some
sort of working or living environment that has not been designed by the individual
himself, but results from some reasoning that is predominantly or wholly beyond
the individual’s understanding. In a situation like this we have, as far as work is
concerned, no “opportunities to direct our attention to interesting, useful, or important
things.” In these conditions habitual behavior dominates the work floor and very little
of the behavior that characterizes the individual in the rest of its life is visible.
This already explains part of the bureaucratic syndrome by answering, at least par-
tially, the question “What shuts down so much of a bureaucrat’s mental capabilities?”
The partial answer is that a difficult to understand environment that effectively acti-
vates habitual behavior leads to the activation of habitual behavior while denying the
bureaucrat self-selected opportunities of intrinsic interest, usefulness, or importance.
Consequently, absent the understanding of their significance in the bigger scheme
of things, the true bureaucrat has no real responsibilities other than maintaining the
conditions in which habitual functioning is facilitated, which is exactly what I saw
in the introductory example.
The conclusion that the bureaucrat’s single or main—self-imposed—
responsibility is to uphold the conditions for its own habitual functioning explains
to a large degree the stability of bureaucracies. But note that this is especially the
case for work environments that exceed the scope of understanding of workers and
management: only here they have no choice but to uphold the conditions in which
they function habitually. With sufficient organizational understanding, workers and
management can break this cycle. We will return to this topic in the subsection on
“Authoritarianism” (Sect. 11.3).
w.jager@rug.nl

11 The Psychological Drivers of Bureaucracy 231
11.3.2 Two Modes of Thought
The previous subsection already separated a habitual mode of thought, which requires
very little attentional control, and forms of cognition that are not (yet) habitual be-
cause they do require highly focused attention, for example because they are new,
ever changing, or otherwise engaging or challenging. This opposition arises from two
large families of cognitive phenomena that McGilchrist (2010) (with extensive justi-
fication and highly compelling historical support) couples to the left and right brain
hemispheres. In a recent paper (Andringa et al. 2013) we generalized McGilchrist’s
interpretations as two complementary modes of cognition: the coping mode and the
pervasive optimization mode.2
The coping mode is concerned with control: with preventing things (the whole
world actually) from spinning out of control. Problem solving and the suppression of
interfering diversity are central concepts for this mode. The pervasive optimization
mode on the other hand is, as the name suggests, concerned with the optimization of
all processes in the context of everything else. Where the coping mode is concerned
with the problems of the here and the now, the pervasive optimization mode is
concerned with promoting the likeliness of beneficial states in the near and distant
future; both here and elsewhere, and for yourself (body and mind) as well as the rest
of the world (family and friends, and the natural and social environment). Where
the coping mode is highly focused and aims at tangible results in a structured and
predictable way, the pervasive optimization mode is much more diffuse; it has no
sequential demands and does not necessarily lead to directly tangible results. It does
however set-up, in a statistical sense, the conditions for an unproblematic future. The
coping mode relies on situational control and intelligent problem solving skills. The
pervasive optimization mode relies on a broad understanding of the world and its
dynamics in combination with the skills to relate to and work with these dynamics
(Andringa et al. 2013).
The concept of “intelligence,” especially as conceptualized and measured in an
IQ-test, summarizes the coping mode because it measures one’s ability to produce
standardized and expected answers to self-contained problems. Intelligence is proven
through the ability to solve problems posed by others. The minimal capacity to do
this is simply by reproducing and applying appropriate formal operations without
understanding neither the problem nor the situation that gave rise to it. This rule-
application ability—apparent as formalization—is capitalized on in a stereotypical
bureaucracy.
This can be contrasted to the concept of “understanding”—according to the New
Oxford Dictionary “the ability to perceive the significance, explanation, or cause of
2 The term pervasive-optimization mode has been introduced in this paper. In Andringa et al. (2013)
we did not use a single term and we described this mode as cognition for exploration, disorder,
or possibility. In a recent paper “Cognition From Life” (Andringa et al. 2015) we introduced the
term cocreation mode of cognition. We decided to use the term pervasive-optimization mode in this
paper since the term co-creation mode requires additional explanation.
w.jager@rug.nl

232 T. C. Andringa
(something)”—which captures strengths of the pervasive optimization mode. If you
understand something you can use it not only reproductively or in a scripted way,
but you know how to apply it in novel and open application domains. Consequently
you can prove your depth and breadth of understanding through realizing novel or
nonstandard results in the world. Conversely you proof your lack of understanding by
making a mess of your live (indicating the utter failure of pervasive optimization).
Another way to proof your lack of understanding is by reducing your life to an
existence where very little novel or nonstandard happens (e.g., the extension of a
bureaucratic attitude to the rest of life). In positive terms, the discovery of relations
(between everything) and the detection of possibilities (in oneself, in others, at work,
or in the whole of the environment) is strength of the pervasive optimization mode.
Returning to the example I started with. A bureaucrat is unlikely to act bureau-
cratically when not at work and especially not while among friends. The pervasive
optimization mode seems, therefore, the default mode, while the coping mode is
a fall-back mode that shines when the pervasive optimization mode was unable
to prevent immanent or pressing problems. Interpreted as such, a bureaucracy is
a working environment that forces (coerces) employees into a problem-solving,
problem-preventing, or problem-control mode: the coping mode.
As outlined in our earlier paper on Learning Autonomy (Andringa et al. 2013), the
pervasive optimization mode assumes autonomous participation in an open, dynamic,
and infinite world of nested processes that form dynamically stable and continually
evolving entities: the real continually developing and never fully graspable world.
For the pervasive optimization mode of being, truth is defined as accordance with
reality, which is to be tested by acting in the world; as such understanding and
experiences are essentially subjective. This mode of being is particularly effective in
situations where new aspects of the dynamics of the world are to be investigated to
expand one’s thought-action repertoire (Fredrickson and Branigan 2005) and where
novel and creative solutions are appropriate.
In contrast, the coping mode assumes a closed, static, and self-contained (and
therefore finite) world, in which entities are symbolic, discrete, and abstract and in
which perfect solutions may be possible. It is also a mode in which one is an “objec-
tive” observer instead of a participant. It is the world as represented in a computer
program: highly functional, perfectly repeatable, and subject to rational consider-
ations, but ultimately devoid of life. In this mode of being, truth is defined as the
result of consistent reasoning and consensually agreed on linguistically shared and
presented facts. This mode of being is particularly effective in situations in which
(immediate) problems have to be solved or addressed in a detached, rational, stan-
dardized, and communicable way. Bureaucracies, but also scientific communication,
are typical examples of this.
Because the coping mode assumes a closed, static, and self-contained (and there-
fore finite) world it needs an external influence to maintain the conditions in which
it can function in the first place. As we argued in Learning Autonomy, authorities—
defined as processes or agents that create, maintain, and influence the conditions
in which agents exist—fulfill this role. The authority for the left hemispheric coping
mode is either its own right hemisphere or some external authority such as parents,
w.jager@rug.nl

11 The Psychological Drivers of Bureaucracy 233
leaders, governments, or cultural influences in the broadest possible sense. In prac-
tice, it is a combination of internal and external authority, and it defaults to external
authority whenever the right hemisphere is unable to act as reliable authority. Put
differently, when the right hemisphere is unable to generate a sufficient level of un-
derstanding of the situation, it cannot remain in the lead and the left hemisphere
becomes dominant at the cost of surrendering autonomy to some (actually any) ex-
ternal authority. Importantly, this switch is subconscious. Still we can become aware
of it through metacognition (like observing a change of emotions and/or a change
in attitude or strategy). We will return to the role of understanding in the section on
authoritarianism.
Table 11.3 provides a summary of properties ascribed to the coping and the per-
vasive optimization mode. It is based on Table 11.1 of Andringa et al. (2013), which
in turn is based on Chap. 1 of McGilchrist (2010). The remarks in italic are examples
of a bureaucratic (for the coping mode) and a non-bureaucratic (for the pervasive
optimization mode) interpretation of these properties. It will be clear from Table 11.3
that the strengths of the coping mode can be used to illustrate typical and/or extreme
bureaucratic functioning, while the pervasive optimization mode can be used to il-
lustrate a non-bureaucratic alternative. Note that the original table was intended as a
summary of left and right hemispheric strengths to be used in a quite different context:
that it can be used to illustrate typical properties of bureaucratic and non-bureaucratic
organizations, is a serendipitous observation that I consider highly meaningful.
11.3.3 Authoritarianism
At the end of the last subsection, authority was defined as the ability to create,
maintain, influence, or exploit a living environment (Andringa et al. 2013). This
entails that whenever individuals do not know how to self-maintain proper living
conditions, they must rely on some sort of “authority” to keep living conditions
within manageable bounds. This need for authority scales inversely with the scope
of inadequacy: the more pervasive the inadequacy, the greater the need for and role
of authority. Conversely, the better individuals cope with and maintain their own
living environment—the more they have internalized authority—the less they need
external authorities. This essential (and existential) need for authority is the defining
characteristic of the concept of authoritarianism.
Within the domain of political psychology people with a strong need for au-
thority are known as authoritarians and those who do not as libertarians (Stenner
2005, 2009, 2009). Authoritarians prefer (centralized) group authority and unifor-
mity, while libertarians prefer (decentralized) individual authority and diversity. The
structure and properties of authoritarian behavior have been studied in detail in “The
Authoritarian Dynamic” by Princeton researcher Karen Stenner (2005). Authoritar-
ianism is characterized by a strong tendency to maximize oneness (via centralized
or group control) and sameness (via common standards), especially in conditions
where the things that make us one and the same—common authority and shared
values—appear to be under threat.
w.jager@rug.nl

234 T. C. Andringa
T
ab
le
11
.3
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t
“t
h
e
w
o
rl
d
b
ri
n
g
s”
w.jager@rug.nl

11 The Psychological Drivers of Bureaucracy 235
T
ab
le
11
.3
(c
on
ti
nu
ed
)
T
op
ic
C
op
in
g
m
od
e
of
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gn
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io
n
(L
ef
t
he
m
is
ph
er
e)
B
u
re
a
u
cr
a
ti
c
in
te
rp
re
ta
ti
o
n
P
er
va
si
ve
op
ti
m
iz
at
io
n
m
od
e
of
co
gn
it
io
n
(R
ig
ht
he
m
is
ph
er
e)
N
o
n
-b
u
re
a
u
cr
a
ti
c
in
te
rp
re
ta
ti
o
n
In
te
re
st
s
In
te
re
st
ed
in
th
e
fa
m
il
ia
r
an
d
th
e
kn
ow
n,
di
ffi
cu
lt
y
w
it
h
di
se
ng
ag
in
g
fr
om
th
e
fa
m
il
ia
r
In
te
re
st
ed
in
th
e
no
ve
l
F
o
rm
s
a
n
d
p
ro
ce
d
u
re
s
fo
rm
th
e
o
n
ly
o
b
je
ct
o
f
in
te
re
st
A
lw
a
ys
in
te
re
st
ed
in
w
a
ys
to
a
d
a
p
t
th
e
o
rg
a
n
iz
a
ti
o
n
to
a
ch
a
n
g
in
g
a
n
d
d
ev
el
o
p
in
g
w
o
rl
d
C
on
ce
rn
ed
w
it
h
w
ha
t
it
kn
ow
s
C
on
ce
rn
ed
w
it
h
w
ha
t
it
ex
pe
ri
en
ce
s
W
h
a
t
ca
n
n
o
t
b
e
d
ea
lt
w
it
h
in
th
e
bu
re
a
u
cr
a
cy
d
o
es
n
o
t
ex
is
t
N
ew
in
fo
rm
at
io
n,
ne
w
sk
il
ls
,e
m
ot
io
na
l
en
ga
ge
m
en
t
C
on
ce
rn
ed
w
it
h
m
an
-m
ad
e
ob
je
ct
s
C
o
m
p
et
en
ce
d
ev
el
o
p
m
en
t
o
f
w
o
rk
er
s
is
n
o
t
sc
ri
p
te
d
bu
t
B
ec
a
u
se
th
es
e
a
re
ty
p
ic
a
ll
y
st
a
ti
c
a
n
d
fo
r
a
p
a
rt
ic
u
la
r
u
se
d
ev
el
o
p
s
o
n
th
e
jo
b
th
ro
u
g
h
in
d
iv
id
u
a
l
ex
p
er
ie
n
ce
a
n
d
d
ev
el
o
p
m
en
t.
W
o
rk
s
sh
o
u
ld
b
e
in
h
er
en
tl
y
re
w
a
rd
in
g
N
on
li
vi
ng
ob
je
ct
s
sp
ec
ia
li
st
.L
iv
in
g
en
ti
ti
es
as
to
ol
s
or
in
st
ru
m
en
ts
M
or
e
co
nc
er
ne
d
w
it
h
li
vi
ng
in
di
vi
du
al
s.
L
iv
in
g
in
di
vi
du
al
s
as
ot
he
r
in
di
vi
du
al
s
P
eo
p
le
a
n
d
a
n
im
a
ls
re
d
u
ce
d
to
n
u
m
b
er
s
th
a
t
ca
n
m
a
n
ip
u
la
te
d
in
a
si
m
il
a
r
w
a
y
a
s
o
th
er
re
so
u
rc
es
E
a
ch
in
d
iv
id
u
a
l
cu
st
o
m
er
h
a
s
to
b
e
tr
ea
te
d
in
th
e
w
a
y
m
o
st
su
it
a
b
le
fo
r
th
e
in
d
iv
id
u
a
l
w.jager@rug.nl

236 T. C. Andringa
T
ab
le
11
.3
(c
on
ti
nu
ed
)
T
op
ic
C
op
in
g
m
od
e
of
co
gn
it
io
n
(L
ef
t
he
m
is
ph
er
e)
B
u
re
a
u
cr
a
ti
c
in
te
rp
re
ta
ti
o
n
P
er
va
si
ve
op
ti
m
iz
at
io
n
m
od
e
of
co
gn
it
io
n
(R
ig
ht
he
m
is
ph
er
e)
N
o
n
-b
u
re
a
u
cr
a
ti
c
in
te
rp
re
ta
ti
o
n
S
tr
en
gt
hs
T
ho
ro
ug
hl
y
kn
ow
n
an
d
fa
m
il
ia
r
G
at
he
ri
ng
ne
w
in
fo
rm
at
io
n
S
ta
n
d
a
rd
iz
ed
ta
sk
ex
ec
u
ti
o
n
b
y
sp
ec
ia
li
st
s
Im
p
ro
ve
u
n
d
er
st
a
n
d
in
g
o
f
a
ll
re
le
va
n
t
p
ro
ce
ss
es
a
n
d
a
sp
ec
ts
o
f
th
e
jo
b
E
ffi
ci
en
t
in
ro
ut
in
e
si
tu
at
io
ns
an
d
fa
m
il
ia
r
sk
il
ls
G
oo
d
w
he
n
pr
ed
ic
ti
on
is
di
ffi
cu
lt
T
ra
in
in
g
to
re
d
u
ce
er
ro
r
fr
eq
u
en
cy
F
le
xi
b
le
ta
sk
ex
ec
u
ti
o
n
b
y
ge
n
er
a
li
st
s
P
ri
or
it
iz
es
th
e
ex
pe
ct
ed
an
d
ge
ne
ra
te
s
ex
pe
ct
at
io
ns
A
no
m
al
y
(i
nd
iv
id
ua
li
ty
)
de
te
ct
or
:
in
di
vi
du
al
s
H
el
p
st
a
n
d
a
rd
cu
st
o
m
er
s
fi
rs
t,
ir
re
sp
ec
ti
ve
o
f
u
rg
en
cy
A
d
a
p
t
o
rg
a
n
iz
a
ti
o
n
to
th
e
si
tu
a
ti
o
n
T
hi
ng
s
m
ad
e
fi
xe
d
an
d
eq
ui
va
le
nt
:
ty
pe
s.
A
ll
th
at
is
re
-p
re
se
nt
ed
as
ov
er
-f
am
il
ia
r,
in
au
th
en
ti
c,
li
fe
le
ss
ca
te
go
ri
es
M
or
e
ef
fi
ci
en
tl
y
w
he
n
in
it
ia
l
as
su
m
pt
io
ns
ne
ed
to
be
re
vi
se
d
or
w
he
n
ol
d
in
fo
rm
at
io
n
ne
ed
s
to
be
di
st
in
gu
is
he
d
fr
om
ne
w
in
fo
rm
at
io
n.
A
ll
th
at
is
“p
re
se
nt

as
ne
w
,a
ut
he
nt
ic
,a
nd
in
di
vi
du
at
ed
E
q
u
a
te
p
eo
p
le
w
it
h
(c
a
se
)
n
u
m
b
er
s.
G
u
a
ra
n
te
ed
eq
u
a
li
ty
in
tr
ea
tm
en
t
o
f
a
ll
ca
se
s
T
h
e
a
b
il
it
y
to
g
u
a
ra
n
te
e
th
a
t
th
e
so
ci
et
a
l
g
o
a
l
is
co
n
tr
ib
u
te
d
to
,
ir
re
sp
ec
ti
ve
th
e
si
tu
a
ti
o
n
o
r
th
e
cu
st
o
m
er
P
re
fe
re
nc
es
P
re
fe
re
nc
es
fo
r
th
in
gs
th
at
ar
e
re
pr
es
en
te
d
as
re
la
ti
ve
ly
in
va
ri
an
t
ac
ro
ss
sp
ec
ifi
c
in
st
an
ce
s,
al
lo
w
in
g
fo
r
ab
st
ra
ct
ed
ty
pe
s
or
cl
as
se
s
of
th
in
gs
P
re
fe
re
nc
e
fo
r
th
in
gs
th
at
ex
is
t
in
th
e
w
or
ld
.S
en
si
ti
ve
to
w
ha
t
di
st
in
gu
is
he
s
di
ff
er
en
t
in
st
an
ce
s
of
si
m
il
ar
ty
pe
fr
om
ea
ch
ot
he
r.
P
eo
p
le
(i
n
cl
u
d
in
g
bu
re
a
u
cr
a
ts
)
sh
o
u
ld
a
d
a
p
t
to
th
e
bu
re
a
u
cr
a
cy
.
N
o
t
vi
ce
ve
rs
a
T
h
e
o
rg
a
n
iz
a
ti
o
n
a
d
a
p
ts
it
se
lf
fl
ex
ib
ly
to
th
e
si
tu
a
ti
o
n
a
n
d
/o
r
ch
a
n
ge
s
in
th
e
si
tu
a
ti
o
n
A
tt
en
ti
on
ty
pe
L
oc
al
na
rr
ow
ly
se
le
ct
iv
e
(h
ig
hl
y)
fo
cu
se
d
at
te
nt
io
n
B
ro
ad
,g
lo
ba
l
an
d
fl
ex
ib
le
at
te
nt
io
n
O
n
ly
sp
en
d
ti
m
e
o
n
fo
rm
a
l
ro
le
s
a
n
d
fo
rm
a
l
p
ro
ce
d
u
re
s.
U
n
a
b
le
(a
n
d
u
n
in
te
re
st
ed
)
to
fo
re
se
e
co
n
se
q
u
en
ce
s
S
p
en
d
ti
m
e
o
n
th
e
ro
le
a
n
d
im
p
a
ct
o
f
th
e
o
rg
a
n
iz
a
ti
o
n
in
th
e
co
n
te
xt
o
f
th
e
la
rg
er
so
ci
et
y
w.jager@rug.nl

11 The Psychological Drivers of Bureaucracy 237
T
ab
le
11
.3
(c
on
ti
nu
ed
)
T
op
ic
C
op
in
g
m
od
e
of
co
gn
it
io
n
(L
ef
t
he
m
is
ph
er
e)
B
u
re
a
u
cr
a
ti
c
in
te
rp
re
ta
ti
o
n
P
er
va
si
ve
op
ti
m
iz
at
io
n
m
od
e
of
co
gn
it
io
n
(R
ig
ht
he
m
is
ph
er
e)
N
o
n
-b
u
re
a
u
cr
a
ti
c
in
te
rp
re
ta
ti
o
n
C
on
st
ru
ct
io
n
of
w
or
ld
S
ta
rt
w
it
h
pi
ec
es
an
d
pu
t
th
es
e
to
ge
th
er
.B
ot
to
m
-u
p
S
ta
rt
fr
om
th
e
w
ho
le
an
d
go
,i
f
re
qu
ir
ed
,i
nt
o
de
ta
il
.T
op
-d
ow
n
O
rg
a
n
iz
e
a
lo
n
g
fo
rm
a
l
ro
le
s
a
n
d
w
o
rk
-b
re
a
kd
o
w
n
st
ru
ct
u
re
S
ta
rt
w
it
h
so
ci
et
a
l
ro
le
o
f
th
e
o
rg
a
n
iz
a
ti
o
n
R
ep
re
se
nt
at
io
n
of
ob
je
ct
s
P
re
fe
re
nc
e
to
re
-p
re
se
nt
ca
te
go
ri
es
of
th
in
gs
,a
nd
ge
ne
ri
c,
no
ns
pe
ci
fi
c
ob
je
ct
s
In
di
vi
du
al
un
iq
ue
in
st
an
ce
s
of
th
in
gs
an
d
in
di
vi
du
al
ge
ne
ri
c
ob
je
ct
s:
in
di
vi
du
al
s
ar
e
G
es
ta
lt
w
ho
le
s
P
eo
p
le
a
n
d
ta
sk
s
a
s
n
u
m
b
er
s
o
r
ca
se
s.
V
a
ri
a
ti
o
n
s
b
et
w
ee
n
ca
se
s
su
p
p
re
ss
ed
U
n
iq
u
en
es
s
o
f
p
eo
p
le
a
n
d
ta
sk
s
d
efi
n
es
th
e
a
p
p
ro
a
ch
.V
a
ri
a
ti
o
n
b
et
w
ee
n
ca
se
s
a
s
g
u
id
el
in
e
S
ol
ut
io
n
li
m
it
at
io
ns
P
ro
bl
em
so
lv
in
g:
si
ng
le
so
lu
ti
on
an
d
la
tc
h
on
to
th
at
A
rr
ay
of
po
ss
ib
le
so
lu
ti
on
s,
w
hi
ch
re
m
ai
n
li
fe
w
he
n
al
te
rn
at
iv
es
ar
e
ex
pl
or
ed
A
ll
a
ct
iv
it
ie
s
fr
a
m
ed
a
s
p
ro
b
le
m
-s
o
lv
in
g
w
it
h
a
si
n
g
le
o
p
ti
m
a
l
so
lu
ti
o
n
A
ct
iv
it
ie
s
a
re
fr
a
m
ed
a
s
a
co
n
ti
n
u
a
l
o
p
ti
m
iz
a
ti
o
n
p
ro
ce
ss
o
f
th
e
w
h
o
le
o
rg
a
n
iz
a
ti
o
n
g
iv
en
it
s
so
ci
et
a
l
ro
le
D
en
y
in
co
ns
is
te
nc
ie
s.
S
up
pr
es
si
ng
no
t
cu
rr
en
tl
y
re
le
va
nt
re
la
ti
on
s
A
ct
iv
el
y
w
at
ch
in
g
fo
r
di
sc
re
pa
nc
ie
s
M
is
m
a
tc
h
es
b
et
w
ee
n
bu
re
a
u
cr
a
ti
c
re
a
li
ty
a
n
d
a
ct
u
a
l
re
a
li
ty
a
re
se
tt
le
d
in
fa
vo
r
o
f
th
e
bu
re
a
u
cr
a
ti
c
re
a
li
ty
D
is
cr
ep
a
n
ci
es
b
et
w
ee
n
re
a
li
ty
a
n
d
ex
p
ec
ta
ti
o
n
s
se
en
a
s
a
le
a
rn
in
g
o
p
p
o
rt
u
n
it
y
P
re
fe
rr
ed
kn
ow
le
dg
e
ty
pe
A
ffi
ni
ty
w
it
h
pu
bl
ic
kn
ow
le
dg
e
P
er
so
na
l
kn
ow
le
dg
e
D
ec
is
io
n
s
b
a
se
d
o
n
w
ri
tt
en
re
co
rd
D
ec
is
io
n
s
b
a
se
d
o
n
in
d
iv
id
u
a
l
ex
p
er
ie
n
ce
a
n
d
u
n
d
er
st
a
n
d
in
g
M
ai
n
em
ot
io
ns
E
m
ot
io
ns
as
so
ci
at
ed
w
it
h
co
m
pe
ti
ti
on
,r
iv
al
ry
,
in
di
vi
du
al
-s
el
f-
be
li
ev
e
(p
os
it
iv
e
an
d
ne
ga
ti
ve
)
A
ll
em
ot
io
ns
.E
m
ot
io
ns
re
la
te
d
to
bo
nd
in
g
an
d
em
pa
th
y
E
m
o
ti
o
n
s
a
ss
o
ci
a
te
d
w
it
h
bu
re
a
u
cr
a
ti
c
in
fi
g
h
ti
n
g,
p
re
se
rv
in
g
o
n
e’
s
p
u
b
li
c
fa
ce
,
co
m
p
et
it
io
n
w
it
h
in
a
n
d
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238 T. C. Andringa
Table 11.4 Child rearing qualities used to determine authoritarianism
Authoritarians
Children should:
Libertarians
Children should:
Should obey parents Be responsible for their actions
Have good manners Have good sense and sound judgment
Be neat and clean Be interested in how and why things happen
Have respect for elders Think for themselves
Follow the rules Follow their own conscience
Stenner (2005) used the 5 two-option questions about child rearing values to
determine the degree of authoritarianism that are depicted in Table 11.4.
The difference between the answers that authoritarians and libertarians choose is
qualitative: authoritarians teach children to behave in certain proscribed ways and to
obey external authorities (elders, parents, norms), libertarians teach children how to
understand the world and how to act responsibly and autonomously. The difference
between authoritarians and libertarians is, therefore, neither ideological nor political:
it depends on a combination of two aspects (1) internal or external authority, and
(2) the depth and pervasiveness of understanding of the current living environment.
Authoritarianism is, therefore, both, a personality trait and a state-of-being that is
manifested in some situations, but not in others: the more individuals are brought into
situations they do not (have learned to) understand and the more they are pressured to
act, the more they will exhibit authoritarian behavior (See subsection Authoritarian
Dynamic).
The child rearing qualities reflect the conditions that were identified for the left
hemispheric coping mode and the right hemispheric pervasive optimization mode.
As such it makes sense to interpret authoritarian behavior as behavior guided by
the logic of the coping mode and libertarian behavior as behavior guided by the
pervasive optimization mode. It also follows that bureaucracy is a manifestation of
authoritarianism. Which also explains the reason why even strong bureaucrats are
never bureaucratic among friends: here they are responsible for their own actions,
expected to have a good sense and sound judgment, to be interested in others, to
think and decide for themselves, and to follow their conscience. It is just that their
working environment forces them out of this mode and into the coping mode.
11.3.4 Two Attitudes Toward a Complex World
According to Stenner (2009) authoritarians are not endeavoring to avoid complex
thinking so much as a complex world. Authoritarians are just as intelligent as
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11 The Psychological Drivers of Bureaucracy 239
libertarians3, but they understand the world more shallowly and less pervasively.
Consequently, two individuals can experience and interpret a shared world quite
differently. If it is experienced as too complex to comfortably deal with, one is in
a coping or authoritarian mode of being. Consequently one’s highest priority is to
eliminate all sources of diversity to bring complexity down to manageable levels.
And this can explain why people in the authoritarian mode take control over de-
cision processes and become subtly or overtly intolerant to uncontrolled diversity
through, for example, coercive formalization. It is not because they think they can
do it better—although they might be convinced of that—but because of a strong
unconscious urge to establish a larger measure of control over the situation with the
aim to simplify it.
In the libertarian or pervasive optimization mode the complexity of the world is
well below daily coping capacity and where authoritarians see problems they see
opportunities. This can actually be problematic because realizing these opportuni-
ties is bound to lead to further social or organizational complexification that might
aggravate authoritarians even further. Libertarians are therefore, quite unwittingly,
major sources of feelings of inadequacy in authoritarians.
And this leads to a one-sided resentment—a shared and therefore unifying
emotion—toward anything beyond coping capacity among authoritarians of which
libertarians are typically completely unaware. In fact encroaching bureaucracy can
be interpreted as a (low-intensity) war between two ways of facing reality. While
libertarians are unaware of any war being fought (because they fail to see any need
for it), they can be blamed for co-creating a complex world surpassing authoritarian
coping capabilities. And authoritarians, with their limited understanding, share a
deep anxiety and are highly motivated to do something about it collectively.
This subconscious anxiety motivates to oppose all sources of complexity, unpre-
dictability, novelty, and growth that complexify, confuse, and destabilize an ordered
and predictable state of affairs. In fact people in an authoritarian mode want to
distance themselves from all of these things and the people (e.g., immigrants, homo-
sexuals, libertarians) that embody or promote them. One driving emotion is disgust
(Frijda 1986; Inbaret al. 2009): the urge to distance oneself from an unhealthy or
otherwise harmful object, activity, person, or influence. Authoritarians in this state
speak quite frankly and clearly about the moral decline that they see all around them
and that disgusts them (and often enough explicitly worded). And they are quite mo-
tivated to do something about it. Vocal moral outrage about the organization losing
its values and morals (typically in response to some gentle questions about the state
of the organization) is an indication of an organization ready to become dominated
by an authoritarian mindset and the associated urge to bring the complexity of the
world/organization back to within coping capacity.
3 Authoritarians might value intelligence more than libertarians. For example more than half of the
21 Nazi Nuremburg defendants had a superior intelligence (belonging to the most intelligent 3 to
0.2 %) and only one had average intelligence (Zillmer et al. 2013). This suggests that authoritarians
select on intelligence.
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240 T. C. Andringa
In fact this same process seems to occur on a societal scale during revolu-
tions. In his seminal book on revolutions in the nineteenth and twentieth century,
Billington (1980) explicitly mentions the revolutionary’s strong motivation to reduce
complexity when he concludes:
The fascinating fact is that most revolutionaries sought the simple, almost banal aims of
modern secular men generally. What was unique was their intensity and commitment to
realizing them.
Billington concludes that popular revolutions invariably aim to bring society back to
a simpler state of affairs. Those revolutions, equally invariably, seem to coincide with
periods of increased intolerance (against moral violators, freethinkers, or libertarians)
and the rise of bureaucracy will not be surprising. It is all part of the same dynamic.
11.3.5 The Authoritarian Dynamic
This complexity reducing dynamic has a name: it is called the Authoritarian Dynamic
(conform the name of Stenner’s 2005 book). In its original form it was formulated
for the domain of Political Psychology as the correlation:
Intolerance = Authoritarianism × Threat
In this “formula” “Intolerance” refers to intolerance to diversity and in particular
intolerance to (perceived) violations of norms or the normative order. “Authoritarian-
ism” initially (Stenner 2005) referred to how often one chooses the left-side answers
of Table 11.4, which in turn is a (crude) measure of the shallowness of understand-
ing of the world and the need for external (central or group) authority to create or
maintain a world in which one feels adequate. “Threat” refers to the perceived threat
and/or abundance of indicators of moral decline. The multiplication symbol “ × ”
refers to the “and”-condition entailing that for “intolerance to diversity” to become
prominent both authoritarian disposition and perceived threat are required to build
up the motivation to restore order through intolerance (or coercive formalization).
Note that this combination of (1) a low level of understanding of the world—
ignorance— and (2) the threat-induced significance of acting appropriately leads to
deep feelings of personal inadequacy. This entails that the fundamental driver of the
authoritarian dynamic can be reformulated as the “Ignorance Dynamic.”
Motivation to restore personal adequacy = Ignorance × Cost of failure to act appropriately
The deep feelings of personal inadequacy can—from the perspective of the
Authoritarian—only be improved through the realization of a more tightly controlled
and less diverse world. Interestingly, violence researcher Gilligan (1997) argues that
shame, due to the public display of personal failure to act appropriately, is the
root cause of all violence. This is yet another perspective on the coercive nature of
intolerance.
There is a perfectly viable alternative approach to improve one’s deep feelings
of personal inadequacy, but, unfortunately, authoritarians generally do not come up
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11 The Psychological Drivers of Bureaucracy 241
with this among themselves. This alternative is to educate oneself out of feelings of
personal inadequacy through acquiring a deeper and more pervasive understanding
as a basis for more advanced strategies. Shallow understanding in combination with
normal or good intelligence prevents this. The strength of the coping mode’s “in-
telligent” ways of treating problems as self-contained (such as the problems in an
IQ-test) leads authoritarians to redefine or ignore reality until it fits with their current
solution repertoire.
This is another way to understand authoritarian intolerance. It is intolerance
against anything opposing successful coping with an existing solution repertoire.
It is therefore also intolerance against advanced strategies—based on a deeper and
more pervasive understanding—that are not (yet) fully understood. Only when the
threat level and the “cost of failure to act appropriately” diminish, these coping
strategies can be replaced by pervasive optimization strategies. This entails that
whoever controls the threat-level, controls the level of intolerance to diversity and
growth, the moment intolerance becomes dominant, and the number of people in an
authoritarian mode.
The Authoritarian Dynamic can be defined on the level of the individual as well
as on a group or even societal level. A single authoritarian in an organization will
defer its own authority to the more skilled and knowledgeable around. But the same
authoritarian in a context with more authoritarians will be highly motivated to collec-
tively adopt and enforce measures, i.e., introduce coercive formalization, expected to
reduce situational complexity and personal inadequacy. Actually a small, but highly
motivated, fraction of a society might start a revolution to (re)turn to a simpler, more
controlled, and better understood world according to Billington’s (1980) conclusions.
For example one of the slogans of the French revolution Liberté, égalité, fraternité
(freedom, equality, brotherhood), which became the French national motto a century
later, is appealing to the libertarian values of diversity and individual authority. Yet
it is also consistent with an urge to a simpler and better understood state of affairs,
where people are more equal (similar), more brotherly responsible for each other
(more able to keep each other to the norm), and free to define new (narrower) social
norms. In this light it is not at all surprising that the French Revolution included a
period called “the Reign of Terror” in which all perceived opposition to the revolution
was punished at the guillotine. It was the period of about a year in a highly chaotic
revolutionary decade in which intolerance peaked.
Yet the intolerance to diversity of anxious authoritarians is a normal coping re-
sponse to a situation of which the complexity has developed out of coping capacity
of some fraction (per definition the authoritarian fraction) of the population. It is
their good and democratic right to do something about a situation that they perceive
as highly troublesome. The problem is that their understanding of society, compared
to the libertarian fraction, is lower and this may easily lead to the adoption of sub-
optimal or counterproductive strategies. Yet, the feelings of inadequacy and anxiety
that authoritarians share and that unite them are genuine and these deserve to be
taken very seriously. Ideally they should not be ignored or derided by libertarians,
although they neither share nor understand their outlook on reality.
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242 T. C. Andringa
The more pervasive and deeper understanding of libertarians should allow them to
understand authoritarians much better than vice versa. This entails that the libertarian
fraction of society, at least in principle, holds the keys to the way the authoritarian
dynamic will play out. Libertarians can influence the perceived complexity of so-
ciety through coaching, education, and media and they can in some cases respond
adequately to the threats perceived and moral decline experienced by authoritarians.
Simply taking these seriously and addressing the root causes may result in a society
in which considerably less people are in the authoritarian mode. In such a society
many more people feel adequate because they are adequate social actors. The ensu-
ing equality in personal adequacy ensures that most are in the pervasive optimization
mode. This equality enhances overall wellbeing (Wilkinson 2006; Wilkinson and
Pickett 2009) and it minimizes the probability of a concerted action by authoritar-
ians to overthrow the (morally depraved) status quo in favor of a simpler, but also
more regimented and less free society.
11.3.6 The Bureaucratic Dynamic
I will now come to the core and title of this chapter. How to formulate the psycho-
logical enablers of bureaucracy most succinctly? If bureaucracy is a manifestation
of authoritarianism, i.e., the prevalence of the coping mode of thought, within
professional organizations, something similar to the Authoritarian Dynamic or the
“Ignorance Dynamic” should hold. Of course it must be adapted to the particular
context of professional organizations.
My proposal, as variant of the Ignorance and Authoritarian Dynamic, for a
“Bureaucratic Dynamic” is as follows:
Incentive for coercive formalization
= Bureaucracy incentive = Institutional ignorance × Worker cost of failure
In this “formula” the role of “intolerance” and “motivation to restore personal
adequacy” is played by either the “Incentive for coercive formalization” or the “Bu-
reaucracy incentive” as described by Adler and Borys (1996) and summarized in the
left column of Table 11.2. The role of “Authoritarianism” and “Ignorance” is played
by “Institutional ignorance.” This is a measure of how well workers understand the
consequences of their own actions, both within the organization and on the wider
society. Directly associated is their need (often a demand) for guidance in every non-
standard activity. The role of “Threat” and “Cost of failure to act appropriately” is
played by “Worker cost of failure.” In the case of bureaucracy, the threat is not moral
decline, but failing at the job and publicly being revealed as professionally inade-
quate. This threat pertains as much to the worker making the mistake, as it does to the
superior who will be shamed because (s)he did not have the department under control.
Here, again, we have a combination of two factors: (1) institutional ignorance
leads to an abundance of opportunities to fail and (2) (high) cost of failure. The
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11 The Psychological Drivers of Bureaucracy 243
prevalence and seriousness of failure now becomes the measure of personal inade-
quacy. As in the “Ignorance Dynamic” the right side of the Bureaucratic Dynamic
corresponds to deep feelings of personal inadequacy that “can only be improved
through the realization of a more manageable world,” which, according to the logic
of the coping mode, is through coercive formalization.
The Bureaucratic Dynamic, formulated like this, explains both the basic attitude
and all the key properties of coercive formalization so characteristic of bureaucracy
(see Table 11.2). Workers are seen as sources of problems to be eliminated, and
opportunism of (“fools” as) workers is to be feared. A formal system of complex
procedure and guidelines—all strengths of the coping mode of cognition—replaces
worker’s intelligence, skills, and improvisation ability. Deviations from protocol
become suspect. To prevent the natural tendency of workers to use their “good sense
and sound judgment” the whole organization is made nontransparent and whatever
global transparency exists is made highly asymmetrical so that superiors at any
moment can, but not necessarily do, monitor workers so that workers self-impose
limits on their behavior.
In this process capable workers loose their intrinsic motivation (“The job is no
longer fulfilling and it impedes my personal development”) and identified motivation
(“The job is no longer important and its results not satisfying”). These motivations
are replaced by introjected motivation (“I’d better do it otherwise I’ll face unpleasant
consequences”) and external motivation (“I have no choice,” “The protocol says so,”
“The computer says so,” Befehl ist Befehl). Quickly enough this state of being be-
comes habitual. The result is an individual that while at work, has shut down half of its
intellectual potential, is stuck in a situation with minimal personal growth potential,
and is reduced to an automaton-like shadow of a fully functioning human being.
11.3.7 The Psychological Effects on the Bureaucrat
While bureaucracy is annoying and frustrating for the client and costly for society, its
effects might be worse for the bureaucrat. Compared to a worker in a non-bureaucratic
organization, the bureaucrat misses many opportunities to engage in inherently fulfill-
ing activities, to enjoy meaningful activities, to help others, to contribute undeniably
to a better society, and in general to give meaning and significance to life.
What happens to the bureaucrat if these high-level human needs cannot be compen-
sated in the rest of life? What level of life quality will result? Somewhat alarmingly,
the pattern of these effects resembles those of torture. For example, torture victim
therapist Leanh Nguyen (2007) concludes the following:
The most terrible, and intractable, legacy of torture is the killing of desire—that is, of
curiosity, of the impulse for connection and meaning making, of the capacity for mutuality,
of the tolerance for ambiguity and ambivalence.
This description sounds eerily similar to the description of someone indefinitely
locked in the coping mode of cognition. This quote describes a complete inability
to experience curiosity, joy, play, and interpersonal contact. An inability for playful
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244 T. C. Andringa
sensemaking associated with intrinsic motivation. This is replaced by a constant need
for the certainty and formal clarity. It is as if the pervasive optimization mode has
become inaccessible. Does not this resemble the automaton like bureaucrat in the
introductory example?
This comparison between bureaucracy and torture might, at first glance seem a
bit over the top, but remember that both are about subduing the individual to external
authority (just as slavery by the way). Individual autonomy may well be a defining
characteristic for health (see Andringa and Lanser (2013) for the role of freedom over
mind-states in sound annoyance). From that perspective it makes sense to consider
bureaucracy as a low, but prolonged, level of psychological torture that like full-
blown torture, may have a profound and long lasting influence on bureaucrats and
by extension on society. As far as I know, this topic has not deserved the attention it
should have.
11.3.8 Summary of the Psychological Roots of Bureaucracy
In Sect. 2, the psychological enablers of bureaucracy, I have progressively developed
the psychological foundations of bureaucracy by addressing a number of comple-
mentary perspectives from different psychological specialisms. I will summarize its
main results here.
Step one involved the notion of habits. During habitual behavior it is the envi-
ronment that drives behavior. Habits free the higher faculties of mind during routine
tasks and have as such great benefits. If, however, the use of the higher faculties
of mind is discouraged at work, the result is something of an automaton: a half-
empty human shell performing routine tasks, but devoid of compassion, empathy,
and understanding.
In the section called “Two modes of thought” I showed that (proto)typical bu-
reaucratic behaviors fit perfectly with the coping mode of cognition (Table 11.3).
The coping mode is characterized by intelligently solving self-contained problems,
while the pervasive optimization mode is characterized by ever-improving one’s un-
derstanding of the diversity of the world. This leads to two different attitudes toward
“authority.” For the coping mode some (typically external) authority must limit and
constrain the world so that one’s existing solution repertoire can be applied. In the
pervasive optimization mode the individual internalizes the role of authority and be-
comes progressively more self-deciding and autonomous as understanding becomes
more pervasive and deep.
To study the interplay between authority and understanding, I discussed the
phenomenon of authoritarianism as defined by Stenner (2005). This led to the identi-
fication of two attitudes toward the world: the libertarian attitude in which the world
is full of possibilities and an authoritarian attitude in which a lack of understanding
of the world leads to anxiety and feelings of personal inadequacy of which liber-
tarians are generally unaware. These feelings unify and motivate authoritarians to
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11 The Psychological Drivers of Bureaucracy 245
oppose all sources of complexity, unpredictability, novelty, and growth that complex-
ify, confuse, and destabilize a predictable state of affairs. This drives encroaching
bureaucracy.
The emergence of intolerance to (ill-understood) diversity has been summarized
in the “Authoritarian Dynamic” in which “intolerance” scales with the level of ig-
norance (authoritarianism) and “threat-level” as a measure of the significance of not
understanding one’s world. Together these lead to a sense of personal inadequacy.
The strength of the coping mode’s “intelligent” ways of treating all problems as
self-contained (such as the problems in an IQ-test) leads authoritarians to redefine
or ignore reality until it fits with their current solution repertoire.
The digression into political psychology allowed the formulation of a “Bureau-
cratic Dynamic.” The role of intolerance to diversity is apparent as the prominence
of coercive formalization. Conform the Authoritarian Dynamic, this scales with the
product of “institutional ignorance” and “worker cost of failure.” Public shaming in
case of failure is a measure of worker’s inadequacy as a professional and the man-
ager’s inadequacy both as a leader and as a person. This leads, again according to the
logic of the coping mode, to the worker accepting (or demanding) and the manager
instilling more coercive formalization.
However, in this process workers lose their intrinsic motivation and become grad-
ually more extrinsically motivated and the work becomes more and more habitual.
The workers have shut down half of their intellectual potential and are stuck in a
situation with minimal personal growth potential.
This then, finally, leads me to question whether the psychological effects on
bureaucracy on bureaucrats might be an ignored, yet imminently important, psy-
chological and societal problem. The third and last section of this chapter will not
focus on this problem, but on how the societal goals of organizations can be pro-
tected from bureaucracy. Fortunately this may also protect workers from the (likely)
adverse effects of bureaucracy.
11.4 Protecting the Societal Goals of an Organization
The subtitle of this chapter is “Protecting the societal goals of an organization.”
This section addresses this topic for nonprofit organizations because these have a
social mission. In the introductory example the original societal role of the lost-and-
found department was replaced by a new goal: procedural correctness, irrespective
of the state of the world and the implications of following the procedure. As I have
outlined in the previous section this is the result of the coping mode running amok in
an organization conform the “Bureaucratic Dynamic.” This entails that this section
will firstly address a number of management paradigms in relation to their societal
goals and bureaucracy, secondly it describes core features of non-bureaucratic or
libertarian organizations, and thirdly it formulates safeguards against encroaching
bureaucracy. This chapter ends with some reflections and conclusions.
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246 T. C. Andringa
11.4.1 Management Paradigms for Nonprofits
As summarized at the end of the previous section, the psychology of not understand-
ing one’s world and in particular ignorance about one’s working environment and
not overseeing the consequences (both adverse and beneficial) of one’s activities
leads to encroaching bureaucracy through the generation of more self-centered goals
of complexity reduction that progressively erode the focus on the original societal
goals of an organization. For nonprofit organizations, of which the mission aims at
the achievement of social purposes rather than in generating revenues, this entails
that they gradually delegitimize themselves through making their own stability and
survival more important than their original social raison d’être (Moore 2000). Yet
depending on the management paradigm, nonprofits run this risk to varying degrees.
Stoker (2006) describes and summarizes three management paradigms that
neatly fit a progression from organizations based on coping mode rationality to the
rationality of the pervasive optimization mode. I will describe all three.
11.4.1.1 Traditional Public Management
Traditional public management follows the typical Weberian early twentieth-century
template (Weber 1978) in which bureaucracy delivers organizational effectiveness
through four features that Stoker (2006) summarizes as follows:
The first is the placing of officials in a defined hierarchical division of labor. The central
feature of bureaucracy is the systematic division of labor whereby complex administrative
problems are broken down into manageable and repetitive tasks, each the province of a
particular office. A second core feature is that officials are employed within a full-time career
structure in which continuity and long-term advancement is emphasized. Third, the work of
bureaucrats is conducted according to prescribed rules without arbitrariness or favoritism
and preferably with a written record. Finally, officials are appointed on merit. Indeed they
become experts by training for their function and in turn control access, information, and
knowledge in their defined area of responsibility.
The italic emphasis has been added to indicate concepts arising from the logic of the
coping mode.
11.4.1.2 New Public Management
New public management arose as an alternative to the observation “that public ser-
vice organizations tend to be neither efficient in terms of saving public money nor
responsive to consumer needs” (Stoker 2006). As a result it did not arise from posi-
tive motivations, but as a solution to the problems of bureaucracy. Stoker describes
this as follows.
The solution is to fragment monopolistic public service structures and develop incentives
and tools to influence the way that they operate. Key reforms include the introduction of a
purchaser-provider divide within organizations and the development of performance targets
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11 The Psychological Drivers of Bureaucracy 247
and incentives. The aim is to create an organizational home for the client or consumer
voice within the system to challenge the power of producers. Consumers or their surrogate
representatives, commissioners, would have the power to purchase the services they required
and measure performance. The achievement of better performance would be aided by arms-
length systems of inspection and regulation to check not only the spending of public money
but also the delivery of public services against demanding targets.
New public management then seeks to dismantle the bureaucratic pillar of the Weberian
model of traditional public administration. Out with the large, multipurpose hierarchical bu-
reaucracies, new public management proclaims, and in with lean, flat, autonomous organiza-
tions drawn from the public and private sectors and steered by a tight central leadership corps
So the key improvement compared to the traditional model is the explicit role and
importance of the societal function of the organization, but in this case limited to
specific performance targets to be delivered by lean, flat, and autonomous organiza-
tions of which the performance indicators are still fully under control of some sort
of central leadership that is supposed to represent public and private sector interests.
New public management is clearly aware of important drawbacks of Weberian
bureaucracy, yet it is still guided by the logic of the coping mode. However, it has
some indicators of the pervasive optimization mode such as greater worker autonomy
(within the tight constraints of performance indicators) and some, albeit indirect,
representation of consumers and other beneficiaries of the delivered services.
11.4.1.3 Public Value Management
Public value management (Moore 2000) is an emerging new management paradigm
that is not so much a response to an existing paradigm but a formulation of the role of
nonprofits in modern society (Stoker 2006). Public value management is succinctly
formulated as a public value scorecard (Moore 2003) in which an organization should
balance (1) the public value produced by the organization, (2) the legitimacy and
support enjoyed by the organization, and (3) the operational capacity to achieve its
results. In the public value scorecard the performance indicators are translated as
measures of performance. Moore (2003) describes these as follows.
Some of the measures are those we associate with the public value produced by the
organization—the extent to which it achieves its mission, the benefits it delivers to clients,
and the social outcomes it achieves.
Others are associated with the legitimacy and support enjoyed by the organization—the extent
to which “authorizers” and “contributors” beyond those who benefit from the organization
remain willing to license and support the enterprise. These measures can, to some degree,
be viewed as important because they indicate the capacity of the organization to stay in
operation over time. But these measures can also be viewed to some degree as measures of
value creation in themselves. This is particularly true if we recognize that some part of the
value created by nonprofit organizations lies in the opportunities it affords to public spirited
individuals to contribute to causes they care about, and another part lies in the capacity of
the nonprofit organization to link contributing individuals to one another in a common effort
to realized shared social goals.
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248 T. C. Andringa
Still others are associated with the operational capacity the nonprofit organization is relying
on to achieve its results. This includes not only measures of organizational output, but also
of organizational efficiency and fiscal integrity. It also includes measures of staff morale
and capacity, and the quality of the working relationships with partner organizations. And,
it includes the capacity of the organization to learn and adapt and innovate over time.
Where the Weberian bureaucracy follows the logic of the coping mode, these mea-
sures read as the pervasive optimization mode specified to the context of nonprofit
organizations.
The public value scorecard was a response to an earlier score card: Kaplan’s
(Kaplan and Norton 1996) Balanced Scorecard, for the new public management
paradigm through its focus on financial and efficiency measures. The Public Value
Scorecard differs in a number of central aspects that are characteristic of the pervasive
optimization mode. Moore (2000) formulates these differences as follows.
First, in the public value scorecard, the ultimate value to be produced by the organization
is measured in non-financial terms. Financial performance is understood as the means to
an end rather than an end itself. The end in itself is denominated in non-financial social
terms. It also notes that the value produced by the organization may not lie simply in the
satisfaction of individual clients. It can lie, instead, in the achievement of desired aggregate
social outcomes of one kind or another.
Second, the public value scorecard focuses attention not just on those customers who pay for
the service, or the clients who benefit from the organization’s operations; it focuses as well
on the third party payers and other authorizers and legitimators of the nonprofit enterprise.
These people are important because it is they who provide some of the wherewithal that the
organization needs to achieve its results, and whose satisfaction lies in the achievement of
aggregate social states as well as in the benefits delivered to individual clients.
Third, the public value scorecard focuses attention on productive capabilities for achieving
large social results outside the boundary of the organization itself. Other organizations ex-
isting in a particular industry are viewed not as competitors for market share, but instead as
partners and co-producers whose efforts should be combined with the effort of the nonprofit
enterprise to produce the largest combined effect on the problem that they are jointly trying
to solve. In short, a nonprofit organization should measure its performance not only by its
ability to increase its market share, but also by its ability to strengthen the industry as a whole.
Again I have added emphasis in italic to stress some the core concepts of this ap-
proach. The reader can combine these with the italic remarks in the right column
of Table 11.3 that interprets the strong points of the pervasive optimization mode in
organizational terms. It will be clear that this description matches the properties of
the pervasive optimization mode.
Stoker (2006) concludes that public value management rests “on a fuller and
rounder vision of humanity than does either traditional public administration or new
public management.” He identifies a key difference, namely the role of motivation,
when he concludes:
Ultimately, the strength of public value management is seen to rest on its ability to point to
a motivational force that does not solely rely on rules or incentives to drive public service
practice and reform. People are, it suggests, motivated by their involvement in networks and
partnerships, that is, their relationships with others formed in the context of mutual respect
and shared learning. Building successful relationships is the key to networked governance
and the core objective of the management needed to support it.
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11 The Psychological Drivers of Bureaucracy 249
In terms of motivation, the public value management relies on the power of identified
(“I find it important”) and intrinsic (“I enjoy doing it”) motivation. And this can be
contrasted to the bureaucratic extreme in which the motivations are mainly extrin-
sic (“I have no choice”) or introjected (“I’d better do it otherwise I face negative
consequences”). The positive motivations are associated with (not only) experiential
learning (Andringa et al. 2013; Vygotskiı̆ 1978) and the growth of organization un-
derstanding, which is, conform the Bureaucratic Dynamic, the key protector against
institutional ignorance.
11.4.1.4 Summarizing Key Properties of the Three Management Paradigms
Table 11.5 provides a summary, adapted from Stoker (2006), to which I have added
six rows describing properties in terms of the strengths of the coping and the pervasive
optimization mode.
11.4.2 Libertarian Organizations
Until now I have focused mostly on bureaucracy and the personal, organizational, and
societal manifestations of the coping mode. But how does the pervasive optimization
mode manifest itself in the context of organizations? Stoker (2006) notes that for
public value management to work the motivation of workers needs to be “intrinsic”
or “identified,” which complies with the organic organization type identified by
Adler and Borys (1996). Alternatively one might call organizations that realize this
“Libertarian organizations” because the members are dominated by intrinsic and
identified motivation, understand what they are doing, are autonomous self-deciders,
and, in summary, rely mostly on the pervasive optimization mode of cognition.
Organizational structures that effectively contribute to an ever-changing real world
of dangers and opportunities need flexible access to the available competence and en-
thusiasm. Libertarian organizations must therefore match the available competences
and institutional understanding to whatever the world demands of the organization.
Where authoritarian organizations realize (at best) proscribed results and predictable
mediocrity, libertarian organizations can realize personal growth, institutional ex-
cellence, and with that effective contributions to the wider society. They are truly
optimizing pervasively.
In libertarian organizations the formal hierarchy is as important as in a bureau-
cracy, but its role is quite different: it has to manage autonomy instead of enforcing
compliance. For superiors who know how to manage motivations and how to con-
vey the role of the organization in society, this is not at all demanding because the
very autonomy and commitment of a healthy libertarian organization ensures that it
can deal with stability (where efficiency and organizational optimization are priori-
ties) and change (where protection of quality and the realization of opportunities are
prominent).
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250 T. C. Andringa
Table 11.5 Management paradigms. (Adapted from Stoker (2006), which is based on Kelly et al.
(2002). The lowest 6 rows have been added as interpretations of the original table in terms of the
discourse of this chapter.)
Key objectives Traditional public ad-
ministration
New public manage-
ment
Public value manage-
ment
Role of managers Politically provided in-
puts; services moni-
tored through bureau-
cratic oversight
Managing inputs and
out- puts in a way that
ensures economy and
responsiveness to con-
sumers
The overarching goal is
achieving public value
that in turn involves
greater effectiveness in
tackling the problems
that the public most
cares about; stretches
from service delivery to
system maintenance
Definition of public
interest
To ensure that rules and
appropriate procedures
are followed
To help define and meet
agreed performance tar-
gets
To play an active role in
steering networks of de-
liberation and delivery
and maintain the overall
capacity of the system
Approach to public
service ethos
By politicians or ex-
perts; little in the way
of public input
Aggregation of individ-
ual preferences, in prac-
tice captured by senior
politicians or managers
supported by evidence
about customer choice
Individual and public
preferences produced
through a complex pro-
cess of interaction that
involves deliberative
reflection over inputs
and opportunity costs
Preferred system for
service delivery
Public sector has
monopoly on service
ethos, and all public
bodies have it
Skeptical of public sec-
tor ethos (leads to in-
efficiency and empire
building); favors cus-
tomer service
No one sector has a
monopoly on public
service ethos; main-
taining relationships
through shared values
is seen as essential
Contribution of the
democratic process
Hierarchical depart-
ment or self-regulating
profession
Private sector or tightly
defined arms-length
public agency
Menu of alternatives
selected pragmatically
and a reflexive ap-
proach to intervention
mechanisms to achieve
outputs
Interpretation in
terms of cognitive
modes
Typical of the coping
mode
Aware of limitations of
the coping mode
Transition to the perva-
sive optimization mode
Role of worker Skilled obedience Responsible for as-
signed tasks and
maintaining skills.
Customer oriented
Co-responsible for
societal role execution
and the adaptation
of the organization’s
changing societal
demands
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11 The Psychological Drivers of Bureaucracy 251
Table 11.5 (continued)
Key objectives Traditional public ad-
ministration
New public manage-
ment
Public value manage-
ment
Skills of worker Precision in role exe-
cution (aimed at error
prevention)
Deep understanding of
tasks and role skills
Deep understanding of
role skills and broad un-
derstanding of impact
of own activities on
public value
Motivations Extrinsic and
introjected
Extrinsic, introjected,
identified, and intrinsic.
Role of motivation not
central
Identified and intrinsic.
Essential role of moti-
vation
Attitude to work Obedient and
unengaged
Professional
development
Personal development
In healthy libertarian organizations everyone develops in terms of (institutional)
understanding. This entails that eventually everyone can “play” a diversity of formal
and functional roles. Basically the only real requirements for a healthy libertarian
organization is that everyone in the organization has roles that are often intrinsically
motivating, are generally satisfying, and that do not exceed understanding capacity.
An organization that satisfies these conditions will remain in a pervasive optimization
mode, even in the face of great organizational or societal challenges.
Table 11.6 provides a selection of properties of libertarian organizations formu-
lated to promote the pervasive optimization mode in organizations.
11.4.3 The Dynamics of Encroaching Bureaucracy
We have probably all been members of a team that functioned amazingly well for
a time, but then started to dysfunction and eventually disintegrated. This is because
excellence is fragile: it not only delivers pervasive optimization, but also depends on
it. In his analysis of how twentieth-century (American) bureaucrats took over educa-
tion from teachers, Labaree (2011) describes how the “pedagogically progressive”
vision of education—child-centered, inquiry based, and personally engaging—is a
fragile hot-house flower because it depends on broadly realized favorable conditions
(i.e., successful pervasive optimization). In contrast, the “administrative progres-
sive” vision of education is a weed because it grows under difficult conditions such
as erratic funding, poorly prepared teachers, high turnover, dated textbooks, etc. It
is robust “because its primary goal is to be useful in the narrowest sense of the term:
It aims for survival rather than beauty.”
Labaree accounts a “battle” between the philosopher John Dewey and educational
reformer David Snedden. As proponent of the pedagogically progressive vision, John
Dewey formulated a complex and nuanced narrative of education as a means to make
“workers the masters of their own industrial fate.” In contrast, David Snedden as the
champion of the administrative progressive approach, saw education as vocational
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252 T. C. Andringa
Table 11.6 Properties of libertarian organizations. Intended to stimulate the pervasive optimization
mode of cognition
Topic Property
Vision A “lived” vision of the goals and roles of the organization is widely shared.
It allows everyone in the organization to contribute to its realization via
well-formulated procedures and competent improvisation alike
Approach the organization holistically: optimize everything in context of the
whole; prevent at all cost strict compartmentalization of responsibilities and
information, because specialism and other forms of close-mindedness are
seeds of stagnation and corruption
Motivation Promote and ensure a predominance of intrinsic and identified motivations
Allow people to be happy or enthusiastic about what they have done well
and allow them to repair and learn from mistakes
Competences Focus on pervasive competence development
Promote a deep insight in the societal effects of individual work and the
organization as a whole
Stimulate overlapping competences to ensure organizational redundancy,
optimization opportunities, more timely services, and enhanced work
satisfaction
Distribute responsibilities according to available competences, interests,
ambitions, and enthusiasm. Ignore hierarchical considerations
Be alert of indications of low competence, stagnated development, insensi-
tivity to adverse consequences of (in)action, low inherent motivation, low
commitment to the organization and the services it should provide (e.g.,
9-to-5 mentality), and indicators of lack of enthusiasm
Autonomy The task of management is to manage worker autonomy
Competent autonomy of workers is success indicator
Put real responsibility in every job description and allow a diversification or
responsibilities as competence grows
Stimulate expertise, but prevent specialization
Information Develop an open information infrastructure
Allow for ample opportunities for unstructured information sharing
The Scottish proverb “When the heart is full the tongue will speak” will
ensure that really important information will be shared
training in preparation for a life of servitude. As a narrow-minded authoritarian, he
understood the world in dualisms and countered nuanced arguments by ignoring
them and by repeating reasonable sounding dogma. Labaree (2011) concludes:
Therefore, the administrative progressive movement was able to become firmly established
and positioned for growth because of Snedden’s flame throwing. Put another way, a useful
idiot, who says things that resonate with the emerging ideas of his era and helps clear the
ideological way for the rhetorical reframing of a major institution, can have vastly more
influence than a great thinker, who makes a nuanced and prescient argument that is out of
tune with his times and too complex to fit on a battle standard.
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11 The Psychological Drivers of Bureaucracy 253
This is how authoritarians gain control. Not by the quality of argument, but by fo-
cusing the discussion, by subtly reinterpreting the goals of an organization in a less
rich manner, by ignoring nuances or replacing them by similar sounding opposi-
tions, and by gradually marginalizing and deriding opposition. When authoritarians
have gained control they start simplifying, harmonizing, focusing, and reorganizing
the organization according to Billington’s (1980) observations on revolutions. The
rhetoric is a convenient tool. But the real objective, albeit rarely acknowledged, is
a simpler, more controlled, and better understood world. Authoritarians bring the
complexity of the world, or in this case national education, down to their level of
understanding of it.
This process matches the Bureaucratic Dynamic that we have formulated.
Incentive for coercive formalization
= Bureaucracy incentive = Institutional ignorance × Worker cost of failure
The true drivers of the bureaucratization process are feelings of personal inadequacy
among workers. In the case of educators like Snedden, these feelings arose from being
lost in the complex world of education in which responsibilities are unclear and the
means to realize them even more. The resulting personal anxiety motivates workers
to reestablish their sense of adequacy whenever possible: at work they are now in an
authoritarian mode. Their colleagues who do understand their responsibilities and
know how to realize them feel no anxiety. They are and remain in a libertarian mode
and are generally unaware of the severity of the anxiety in their (now) authoritarian
colleagues.
The authoritarians gravitate toward each other and start to formulate and promote
a simplified understanding of the roles and aims of the organization. The libertar-
ian opposition against this simplified understanding is of course based on a fuller
understanding of the roles and aims of the organization. But these arguments have
no impact on the authoritarians because, in their eyes, the arguments are addressing
irrelevancies with no relation of their new, simplified, and more tangible understand-
ing of the organization’s scope and aims. While the libertarians waste their time
and energy with progressively more nuanced arguments, the authoritarians find each
other and may at some point take control over the organization.
When they do, they make their level of “institutional ignorance” the norm. And
because they are in the coping mode they will realize this norm according to the logic
of the coping mode (Table 11.3, left side). This will, according to the Bureaucratic
Dynamic, lead to the introduction of more “coercive formalization” and a shift from
being as professional as possible to producing tangible measureable outcomes and
preventing errors in realizing these. Preventing worker failure and publicly displayed
inadequacy becomes more important than professional success.
At the same time the libertarians in the organization discover that many of the
things they used to do—and which still make sense given the logic of the pervasive
optimization mode—are no longer officially endorsed because they are incompatible
with the new simplified norm. In fact the old way of working has become a liability
if it hinders the realization of the new, more tangible, performance measures. What
used to be the highest indicators of professionalism, are now costly ways to fail
as a worker. The new professionalism is rule compliance and not organizational
excellence.
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254 T. C. Andringa
Much of what the libertarian worker motivated, is no longer officially or practically
part of the organization’s core business. The moment the new management initiates a
reorganization, of course according to the logic of the coping mode, the libertarians
are faced with a dilemma: leave with professional dignity or succumb to the new
normal and deskill and comply. Whatever the libertarian chooses, the result is the
same: increased institutional ignorance.
11.4.4 Preventing Bureaucracy
I had the doubtful honor to witness such a process in my university. Only two individ-
uals at key positions in the hierarchy drove the process. Fortunately, it was followed
by repair measures when the whole process overshot and the organizational costs
became too high. This happened after some highly skilled and motivated colleagues
had left and others were on sick-leave. At that point workers simply refused to take
further responsibility and the department almost stopped functioning. This paper is
informed by witnessing this process. Without understanding bureaucracy as well as
I do now, the unfolding process was very difficult to counter. Yet it is possible to
devise effective protective measures. In Table 11.7 I have formulated a number of
“Red Flags” as indicators of encroaching bureaucracy that may be helpful to stop a
bureaucratization process before it becomes self-reinforcing.
According to the Bureaucratic Dynamic, the best protection against bureaucratiza-
tion is preventing worker (including management) ignorance and promoting worker
professionalism instead of preventing worker error. A truly healthy and resilient
organization maintains a sufficient level of institutional understanding and worker
autonomy so that no one feels inadequate and every one contributes to the realization
of the organizations full societal goals and not only to a single or a few “key perfor-
mance objectives.” Yet as the analysis of the three management paradigms shows,
institutional understanding improves over time. For example, the new public value
management paradigm starts from the logic of the pervasive optimization mode in-
stead of the logic of the coping mode as would have been the natural Weberian option
a century ago.
A future informed public might not accept the products of a bureaucratic organiza-
tion because it demonstrates, for all to witness, that its management and workers do
not quite understand what they are doing. In addition, if my expectation is substanti-
ated that bureaucracy leads to high personal and societal costs for bureaucrats, future
societies might simply not accept bureaucracy because it signifies a pathological state
of affairs of which the immediate costs are apparent as reduced quality and efficiency,
while the full personal and societal costs are deferred to future generations. In fact
sustainability arguments might drive this.
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11 The Psychological Drivers of Bureaucracy 255
Table 11.7 Red flags. Early indicators disrupting the pervasive optimization mode
Red Flags
Mission The absence of a shared, living vision about the organization’s goals in a larger
societal context
The advance of a simplified and more focused interpretation of the organizations
mission, typically as a limited number of “key performance objectives”
Leaders Leaders insensitive to reasoned and nuanced arguments by competent individuals
at any position in the organization
Leaders only sensitive to arguments related to goal achievement or procedure.
Realizable goals are preferred over desirable goals
Leaders preferring obedience over autonomy and who curtail work-floor auton-
omy
Bureaucrats promoted to key positions
Competences Neglect of work-floor competences
Demotivation of highly autonomous, competent and committed co-workers
Gradual deterioration of quality of the working environment and worker
motivation
The most competent and committed coworkers leave
Standardization at the cost of curtailing of essential/useful diversity
Uniformization Strong focus on formalities while neglecting (or indefinitely) postponing content
Compartmentalization of information and plans
Mediocracy facilitated
11.4.5 Conclusion and Reflection
In some sense this chapter is about the difference between intelligence and under-
standing as manifestations of, respectively, the coping and the pervasive optimization
mode of cognition. Understanding proofs itself as the ability to set up, in a statistical
sense, the conditions for an unproblematic future and an interesting and fulfilling
life. Failure to do so leads to problematic situations to be solved intelligently. While
understanding shines in an open world, intelligence assumes a closed world of self-
contained problems to be addressed with an existing solution repertoire. Anything
in the way of the solution will be ignored or coercively made irrelevant. While un-
derstanding manifests itself through fostering empathic relations, intelligence, as a
last line of defense, is self-protective, impersonal, and ruthless.
Without understanding the consequences of one’s activities, work is bureaucratic.
Since no one is bureaucratic while not at work and especially not among friends, it
is the work environment that activates bureaucratic behavior. In this chapter, I have
shown that bureaucracy in all it facets can be understood from basic psychology.
Bureaucratic behavior is habitual or intelligent rule following. The bureaucrat obe-
diently performs activities that it understands superficially and values marginally,
but that it does not endorse or feels responsible for. As such the bureaucrat appears
and acts as a dehumanized automaton. It is a pitiful state of being.
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256 T. C. Andringa
The psychological enabler of bureaucracy is a sense of personal inadequacy among
workers resulting from not understanding their work and its consequences. This ac-
tivates the coping mode of cognition and with that an urge to bring the complexity
of the working environment down to more manageable levels through promoting
coercive formalization. This process can be summarized as the Bureaucratic Dy-
namic, which states that the prevalence of coercive formalization depends on the
combination of “Institutional Ignorance” and “Worker cost of failure.”
Fortunately, in the last century society became gradually more aware of the effects
and dangers of bureaucracy. New public management arose as a response to curtail
the adverse effects of Weberian bureaucracy as defining aspect of traditional public
management. Because it is still based on the coping mode of cognition it will not
become a bureaucracy-free alternative. New value management however arises from
the logic of the pervasive optimization mode and it has the potential to achieve
organizational excellence without bureaucracy.
Anti-bureaucratic measures should not only focus on the reduction of the number
of rules and regulations because this still follows the logic of the coping mode. It
should instead focus on motivating workers to understand their professional roles
and to learn to oversee the impact of their activities; not only on the organization, but
also on the wider society. This understanding will lead to a reevaluation of the role of
formalization and will erode the need for coercive formalization. The organization
will no longer focus on preventing errors, but on optimizing the multifaceted societal
roles of the organization in ways that are experienced as important, worthwhile, and
intrinsically motivating for its workers. Yet organizations that function like this are
somewhat fragile and may be eroded from the inside by a fraction of workers that
still have an impoverished understanding of the organization and its societal roles. It
will be important to develop safeguards to prevent this.
Current anti-bureaucratic awareness stems from the observation that bureaucratic
organizations are neither efficient in terms of saving public money nor responsive
to consumer needs. Future research may however proof important adverse effects of
bureaucracy on bureaucrats and on society as a whole. This may expose bureaucracy
for what I think it is: a pathological state of human organization, with equally serious
adverse consequences for the bureaucrat and society as a whole.
In the course of writing this chapter I was struck by the consistency and comple-
mentarity of disparate scientific results. Science produces wonderful observations
and generates deep insights, but it has difficulty in combining these if they originate
from different domains. The transdisciplinary framework presented in this chap-
ter allowed far reaching conclusions through the combination of a number of these
observations and insights.
For example the work of Adler and Borys (1996) and especially their conceptual-
ization of bureaucracy, in terms of the degree and type of formalization (enabling or
coercive), gained theoretical support. The stability of bureaucracies can be explained
through the link between bureaucracy and habitual behavior, since bureaucrats feel
a self-imposed responsibility to maintain the condition in which their habitual func-
tioning is guaranteed. Furthermore, McGilchrist’s (2010) description of the way
the two brain hemispheres understand the world and our conceptualization of the
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11 The Psychological Drivers of Bureaucracy 257
pervasive optimization mode and the coping mode (Andringa et al. 2013), seems to
predict how non-bureaucratic and bureaucratic organizations micromanage. This was
a serendipitous finding that I consider highly relevant. In addition Stenner’s (2005)
conceptualization of authoritarianism—as having a problem with a complex world
(and not with complex thinking)—helped to understand the psychological motiva-
tors of bureaucracy in terms of feelings of personal inadequacy. Finally, Billington’s
(1980) observations about revolutions always aiming for simplicity, helped to under-
stand why well-functioning non-bureaucratic organizations might be eroded from
the inside and turn into a bureaucracy.
All in all, it seems to me that bureaucracy is not just a phenomenon that occurs
in professional organizations. Instead it is just one of many manifestations of the
interplay between understanding and intelligence that are important for every aspect
of live.
Appendix
Some core properties of the bureaucratic syndrome (authoritarian dominated) and
the non-bureaucratic syndrome (libertarian dominated organizations).
Topic Bureaucratic syndrome Non-bureaucratic syndrome
Key properties
Organizational goals Societal goals of the orga-
nization are only adhered in
name, but neither understood
nor clearly implemented
Development of a broadly shared
vision about the societal reason
d’être of the organization and the
way to realize it
Overall strategy Stimulating sameness and one-
ness through standardization
and obedience
Continual skilled improvisation
on the basis of a shared vision and
well-chosen procedures
Competence Ignoring, discouraging, and de-
moralizing competent “subordi-
nates.” Deskilling
Relying on and fostering all
proven and budding competencies
in the organization
Autonomy Subordinate autonomy is not an
option. Obedience is more im-
portant than competence
Autonomy and competence devel-
opment of subordinates expected
Content Complete disregard of content
while favoring form
Content is leading, form a means
Organizational
development
Structures and procedures adapt
to the lowest competence level
Everyone is expected to learn and
grow towards autonomous roles in
organization
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258 T. C. Andringa
Topic Bureaucratic syndrome Non-bureaucratic syndrome
Main conflicts
Stability versus develop-
ment
Stability and other forms of high
predictability leading. This de-
fines the organization
The workers in the organization
are constantly developing their
skills in order to improve all as-
pects of the societal role of the
organization (i.e., quality and ef-
ficiency)
Form versus optimization Obsessed with form and for-
malisms. Centralized optimiza-
tion of standardized and nar-
rowly defined responsibilities
Actively eliciting creative and de-
centralized optimization of or-
ganizational goals. Disregard of
form when counter-productive
Standardization versus di-
versity
Obsession with standardization
and curtailing diversity, at the
cost of quality if quality entails
diversity
Concerned with the overall opti-
mization of all work processes in
context, of which both standard-
ization and increasing diversity
are options
Error versus learning Obsessed with preventing errors
and mistakes. The organization
redefines itself to produce what
it can, not what it should; “race
to the bottom”
Error and correction after error
part of continual creative opti-
mization of work processes
Short versus long term Exclusively short-term (form)
oriented, neither care for nor un-
derstanding of mid of long term
goals. However, what is short-
or mid-terms depends on the role
in the organization
Optimization, by all workers. on
all time-scales and all dimensions
of success
Structural properties
Role of hierarchy Hierarchy formalized and in-
flexible, based on assumed (but
never fully checked) compe-
tence of superiors
Hierarchy task dependent,
and therefore flexible and
competence-based
Perception of authorities Authorities never fundamentally
questioned
Incompetent authorities not ac-
cepted, but coached or dismissed
Locus of control Formation of stable authoritar-
ian cliques, who take control
over the institutional change
processes to prevent further
complexity
Loosely and varyingly linked lib-
ertarians at control positions.
Measures of success Performance measures rede-
fined to what is delivered
Performance measure based on
what should be delivered (given
reason d’être)
Accountability Suppression of all forms of ac-
countability at the higher levels
and prevention of errors and ret-
ribution in case of error at the
lower levels
Accountability part of normal in-
stitutional learning and compe-
tence building
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11 The Psychological Drivers of Bureaucracy 259
Topic Bureaucratic syndrome Non-bureaucratic syndrome
Emotions
Overall role Rationality and “objectivity”
leading. Emotions treated as ir-
relevant source of variation, to
be suppressed
Central role of positive emotions
(compassion, enthusiasm, inter-
est) as key motivators; prominent
negative emotions indicative of
organizational failure
Emotion of workers Motivating emotion negative:
activities guided by the fear of
losing control or being shamed
publically
Motivating emotion positive: ac-
tivities aimed at realizing shared
benefits including personal devel-
opment
Emotions of co-workers Utter disregard of the feel-
ings and emotional wellbeing of
coworkers
Strong focus on the creation of op-
timal working condition in which
coworkers feel optimally moti-
vated to give their best
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w.jager@rug.nl

Chapter 12
Active and Passive Crowdsourcing in
Government
Euripidis Loukis and Yannis Charalabidis
Abstract Crowdsourcing ideas have been developed and initially applied in the
private sector, first in the creative and design industries, and subsequently in many
other industries, aiming to exploit the ‘collective wisdom’ in order to perform difficult
problem solving and design activities. It was much later that government agencies
started experimenting with crowdsourcing, aiming to collect from citizens infor-
mation, knowledge, opinions and ideas concerning difficult social problems, and
important public policies they were designing for addressing them. Therefore, it is
necessary to develop approaches, and knowledge in general concerning the efficient
and effective application of crowdsourcing ideas in government, taking into account
its special needs and specificities. This chapter contributes to filling this research gap,
by presenting two novel approaches in this direction, which have been developed
through extensive previous relevant research of the authors: a first one for ‘active
crowdsourcing’, and a second one for ‘passive crowdsourcing’ by government agen-
cies. Both of them are based on innovative ways of using the recently emerged and
highly popular Web 2.0 social media in a highly automated manner through their
application programming interfaces (API). For each of these approaches, the basic
idea is initially described, followed by the architecture of the required information
and communications technology (ICT) infrastructure, and finally a process model
for its practical application.
12.1 Introduction
The capability of a large network of people, termed as ‘crowd’, networked through
web technologies, to perform difficult problem solving and design activities, which
were previously performed exclusively by professionals, has been initially recog-
nized by private sector management researchers and practitioners, leading to the
development of crowdsourcing (Brabham 2008; Howe 2008). Crowdsourcing ideas
E. Loukis (�) · Y. Charalabidis
University of the Aegean, Samos, Greece
e-mail: eloukis@aegean.gr
Y. Charalabidis
e-mail: yannisx@aegean.gr
© Springer International Publishing Switzerland 2015 261
M. Janssen et al. (eds.), Policy Practice and Digital Science,
Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_12
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262 E. Loukis and Y. Charalabidis
have been initially applied in the private sector, first in the creative and design in-
dustries, and subsequently in many other industries, aiming to exploit the ‘collective
wisdom’ (Surowiecki 2004) in order to perform difficult problem solving and design
activities. This has resulted in the development of a considerable body of knowledge
on how crowdsourcing can be efficiently and effectively performed in the private
sector (comprehensive reviews are provided by Rouse 2010; Hetmank 2013; Ped-
ersen et al. 2013; Tarrell et al. 2013). It was much later that government agencies
started experimenting with crowdsourcing, aiming to collect from citizens informa-
tion, knowledge, opinions and ideas concerning difficult problems they were facing,
and important public policies they were designing, through some first ‘citizensourc-
ing’ initiatives (Hilgers and Ihl 2010; Nam 2012). So there is still limited knowledge
on how crowdsourcing can be efficiently and effectively performed in the special
context of the public sector, much less than in the private sector. Therefore, exten-
sive research is required for the development of approaches and methodologies for
the efficient and effective application of crowdsourcing ideas in government for sup-
porting problem solving and policy making, taking into account its special needs and
specificities. This is quite important, taking into account that social problems have
become highly complex and ‘wicked’, with multiple and heterogeneous stakehold-
ers having different problem views, values and objectives (Rittel and Weber 1973;
Kunzand Rittel 1979); previous research has concluded that information and com-
munications technology (ICT) can be very useful for gaining a better understanding
of the main elements of such problems (e.g. issues, alternatives, advantages and dis-
advantages perceived by various stakeholder groups; Conklin and Begeman 1989;
Conklin 2003; Loukis and Wimmer 2012).
This chapter contributes to filling this research gap, by presenting two approaches
in this direction, which have been developed through extensive previous relevant
research of the authors: a first approach for ‘active crowdsourcing’ (in which govern-
ment has an active role, posing a particular social problem or public policy direction,
and soliciting relevant information, knowledge, opinions and ideas from citizens),
and a second one for ‘passive crowdsourcing’ (in which government has a more pas-
sive role, collecting and analyzing content on a specific topic or public policy that
has been freely generated by citizens in various sources, which is then subjected to
sophisticated processing). Both of them are based on innovative ways of using the
recently emerged and highly popular Web 2.0 social media in a highly automated
manner through their application programming interfaces (API) (which are libraries
provided by all social media, including specifications for routines, data structures,
object classes, and variables, in order to access parts of their functionalities and
incorporate them in other applications).
In particular, the first of them is based on a central ICT platform, which can pub-
lish various types of discussion stimulating content concerning a social problem or
a public policy under formulation to multiple social media simultaneously, and also
collect from them data on citizens’ interactions with this content (e.g. views, ratings,
votes, comments, etc.), both using the API of the utilized social media. Finally, these
interaction data undergo various types of advanced processing (e.g. calculation of
analytics, opinion mining, and simulation modelling) in this central system, in order
to exploit them to support drawing conclusions from them. This approach has been
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12 Active and Passive Crowdsourcing in Government 263
developed mainly as part of the research project PADGETS (‘Policy Gadgets Mash-
ing Underlying Group Knowledge in Web 2.0 Media’—www.padgets.eu), which has
been partially funded by the European Commission.
The second passive crowdsourcing approach is based on a different type of cen-
tral ICT platform, which can automatically search in numerous predefined Web 2.0
sources (e.g. blogs and microblogs, news sharing sites, online forums, etc.), using
their API, for content on a domain of government activity or a public policy under
formulation, which has been created by citizens freely, without any initiation, stim-
ulation or moderation through government postings. Through advanced processing
and analysis of this content in the above platform (using opinion and argument ex-
traction, sentiment analysis and argument summarization techniques), conclusions
can be drawn concerning the needs, issues, opinions, proposals and arguments of cit-
izens on this domain of government activity or public policy under formulation. This
approach is developed as part of the research project NOMAD (‘Policy Formulation
and Validation through Non-moderated Crowdsourcing’—www.nomad-project.eu/),
which is partially funded by the European Commission.
The two approaches presented in this chapter combine elements from management
sciences (concerning crowdsourcing approaches), political sciences (concerning
wicked social problems) and technological sciences (concerning social media ca-
pabilities and API), in order to support problem solving and policy-making activities
of government agencies. We expect that the findings of this research will be in-
teresting and useful to both researchers and practitioners of these three disciplines
who are dealing with the public sector. It should be noted that governments have
been traditionally collecting content created by various social actors about domains
of government activity, social problems or public policies under formulation using
various traditional (offline) practices (e.g. collecting relevant extracts from newspa-
pers); furthermore, they actively solicited relevant opinions and ideas from citizens
(through various offline and online citizens’ consultation channels). However, the
proposed approaches allow government agencies to perform such activities more
extensively and intensively at a lower cost, reaching easily wider and more diverse
and geographically dispersed groups of citizens’ (e.g. collecting relevant content
not only from a small number of top newspapers but also from numerous bigger or
smaller newspapers, blogs, Facebook accounts, etc.; also, interacting actively with
many more citizens than the few ones participating in government consultations),so
that they can gradually achieve mature levels of crowdsourcing. Furthermore, the
proposed approaches allow overcoming the usual ‘information overload’ problems
of the traditional practices, as they include sophisticated processing of the collected
content that extracts the main points of it.
This chapter is organized in seven sections. In ‘Background’ our background is
presented, and then in ‘Research Method’ the research methodology is outlined.
Next, the two proposed approaches for passive and active crowdsourcing by govern-
ment agencies are described in ‘An Active Crowdsourcing Approach’ and ‘A Passive
Crowdsourcing Approach’, respectively. A comparison of them, also with the ‘clas-
sical’ is presented in ‘Comparisons’, while in the final ‘Conclusion’ our conclusions
are summarized.
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264 E. Loukis and Y. Charalabidis
12.2 Background
12.2.1 Crowdsourcing
The great potential of the ‘collective intelligence’, defined as a ‘form of universally
distributed intelligence, constantly enhanced, coordinated in real time, and resulting
in the effective mobilization of skills’, (Levy 1997), to contribute to difficult prob-
lem solving and design activities has lead to the emergence of crowdsourcing and
its adoption, initially in the private sector, and subsequently (still experimentally)
in the public sector as well. Crowdsourcing is defined as ‘the act of a company or
institution taking a function once performed by employees and outsourcing it to an
(and generally large) network of people in the form of an open call’ (Howe
2006), or as ‘a new web-based business model that harnesses the creative solutions
of a distributed network of individuals’, in order to exploit ‘collective wisdom’ and
mine fresh ideas from large numbers of individuals (Brabham 2008). While the use
of the collective intelligence of a large group of people as a help for solving difficult
problems is an approach that has been used for long time (Surowiecki 2004; Howe
2008), it is only recently that crowdsourcing started being widely adopted as a means
of obtaining external expertise, accessing the collective wisdom and creativities res-
ident in the virtual crowd. The capabilities provided by the development and wide
dissemination of ICT seem to have played an important role for this, as they allow
the efficient participation and interaction of numerous and geographically dispersed
individuals, and also the analysis of their contributions (Geiger 2012; Zhao and Zhu
2012; Majchrzak and Malhotra 2013). Brabham (2008), based on the analysis of sev-
eral cases of crowd wisdom at work, which resulted in successful solutions emerging
from a large body of solvers, concludes that ‘under the right circumstances, groups
are remarkably intelligent, and are often smarter than the smartest people in them’,
due to the diversity of opinion, independence, decentralization and aggregation that
characterize such a crowd.
Crowdsourcing started being applied initially in the creative and design industries,
and then it expanded into other private sector industries, for solving both mundane
and highly complex tasks. It gradually becomes a useful method for attracting an
interested and motivated group of individuals, which can provide solutions superior in
quality and quantity to those produced by highly knowledgeable professionals. Such
a crowd can solve scientific problems that big corporate R&D groups cannot solve,
outperform in-house experienced geophysicists of mining companies, design original
t-shirts resulting in very high sales, and produce highly successful commercials
and fresh stock photography against a strong competition from professional firms
(Surowiecki 2004; Howe 2006, 2008; Brabham 2008, 2012). This can result in a
paradigm shift and new design and problem solving practices in many industries.
For these reasons there has been significant research interest on crowdsourcing,
which has resulted in a considerable body of knowledge on how crowdsourcing can
be efficiently and effectively performed in the private sector; reviews of this literature
are provided by Rouse (2010), Hetmank (2013), Pedersen et al. (2013) and Tarrell
et al. (2013). Initially this research focused on analyzing successful cases, while later
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12 Active and Passive Crowdsourcing in Government 265
it started generalizing, based on the experience of multiple cases, in order to identify
patterns and trends in this area and also to develop effective crowdsourcing practices.
A typical example in this direction is the study by Brabham (2012), which, based
on the analysis of several case studies, identifies four dominant crowdsourcing ap-
proaches: (i) the knowledge discovery and management approach (= an organization
tasks crowd with finding and reporting information and knowledge on a particular
topic), (ii) the broadcast search approach (= an organization tries to find somebody
who has experience with solving a rather narrow and rare empirical problem), (iii) the
peer-vetted creative production approach (= an organization tasks crowd with creat-
ing and selecting creative ideas), and (iv) the distributed human intelligence tasking
(= an organization tasks crowd with analyzing large amounts of information). Het-
mank (2013), based on a review of crowdsourcing literature, identifies a basic process
model of it, which consists of ten activities: define task, set time period, state reward,
recruit participants, assign tasks, accept crowd contributions, combine submissions,
select solution, evaluate submissions and finally grant rewards. Also, he identifies
a basic pattern with respect to the structure of crowdsourcing Information System
(IS), which includes four main components that perform user management (pro-
viding capabilities for user registration, user evaluation, user group formation and
coordination), task management (providing capabilities for task design and assign-
ment), contribution management (providing capabilities for contributions evaluation
and selection) and workflow management (providing capabilities for defining and
managing workflows), respectively. Furthermore, there are some studies that attempt
to generalize the experience gained from successful applications of crowdsourcing
ideas in order to develop effective practices for motivating individuals to participate
(Brabham 2009; Stewart et al. 2009).
Rouse (2010), based on a review of relevant literature, distinguishes between two
types of crowdsourcing with respect to participants’ motivation: (i) individualistic
(aiming to provide benefits to specific persons and firms), (ii) community oriented
(aiming to benefit a community of some kind, through ideas and proposals), and
(iii) mixed (combinations of the above). Furthermore, she proceeds with identifying
seven more detailed types of participant motivations: learning, direct compensation,
self-marketing, social status, instrumental motivation (= motivation to solve a per-
sonal or firm problem, or to address a personal/firm need), altruism (= motivation to
help the community without personal benefit) and token compensation (= earning a
small monetary prize or gift). Also, the same publication concludes that many of the
benefits of crowdsourcing described in the literature are similar to those of the ‘main-
stream’ outsourcing: cost savings, contracts and payments that are outcome based
(rather than paid ‘per hour’), and access to capabilities not held in-house; an addi-
tional benefit of crowdsourcing, which is not provided by outsourcing, is the capacity
to exploit knowledge and skills of volunteers who might not, otherwise, contribute.
However, at the same time it is emphasized that—as with all outsourcing—the de-
cision to crowdsource should only be made after considering all the production,
coordination and transaction costs, and the potential risks. Many of the highly publi-
cized crowdsourcing successes have been achieved by organizations with substantial
project management and new product/services development systems and capabilities,
which lead to low levels of crowdsourcing coordination and transaction costs.
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266 E. Loukis and Y. Charalabidis
12.2.2 Public Sector Application
Crowdsourcing ideas, as mentioned above, have been initially developed and applied
in the private sector, however later some government agencies started experimenting
with them. Highly influential for this have been central top-down initiatives in several
countries, such as the ‘Open Government Directive’ in the USA (Executive Office
of the President 2009). It defines transparency, participation and collaboration as the
main pillars of open government:
a. Transparency promotes accountability by providing the public with information
about what the government is doing.
b. Participation allows members of the public to contribute ideas and expertise so
that their government can benefit from information and knowledge that is widely
dispersed in society, in order to design better policies.
c. Collaboration improves the effectiveness of government by encouraging partner-
ships and cooperation within the federal government, across levels of government,
and between the government and private institutions.
Crowdsourcing can be quite valuable for promoting and developing two of these
three main pillars of open government: participation and collaboration. This has
lead government organizations, initially, in the USA and later in other countries as
well, to proceed to some first crowdsourcing initiatives, having various forms of
‘citizensourcing’ for collecting information on citizens’ needs and for the solution of
difficult problems. These initiatives motivated some first research in this area, which
aims to analyze these initiatives in order to learn from them, and to identify common
patterns and trends (Lukensmeyer and Torres 2008; Hilgers and Ihl 2010; Nam 2012).
Lukensmeyer and Torres (2008) conclude that citizen sourcing may become a new
source of policy advice, enabling policy makers to bring together divergent ideas
that would not come from traditional sources of policy advice; furthermore, it may
change the government’s perspective on the public from an understanding of citizens
as ‘users and choosers’ of government programs and services to ‘makers and shapers’
of policies and decisions. Hilgers and Ihl (2010) developed a high-level framework
for the application of citizen sourcing by government agencies, which consists of
three tiers:
1. Citizen ideation and innovation: this first tier focuses on the exploitation of the
general potential of knowledge and creativity within the citizenry to enhance the
quality of government decisions and policies, through various methods, such as
consultations and idea and innovation contests.
2. Collaborative administration: the second tier explicitly addresses the integration
of citizens for enhancing existing public administrative processes.
3. Collaborative democracy: this tier includes new ways of collaboration to improve
and expand public participation within the policy process, including the incorpo-
ration of public values into decisions, improving the quality of decisions, building
trust in institutions and educating citizens.
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12 Active and Passive Crowdsourcing in Government 267
Nam (2012), based on the study of citizen-sourcing initiatives in the USA, developed
a framework for the description and analysis of such initiatives, which consists of
three dimensions: purpose (it can be for image making, information creation, service
co-production, problem solving and policy-making advice), collective intelligence
type (professionals’ knowledge or nonprofessionals’ innovative ideas), and govern-
ment 2.0 strategy (it can be contest, wiki, social networking, or social rating and
voting).
However, since public-sector crowdsourcing is still in its infancy, having much
less maturity than private-sector crowdsourcing, further research is required in this
area; its main priority should be the development of approaches and methodologies
for the efficient and effective application of crowdsourcing ideas in government
for supporting problem solving and policy making, taking into account its special
needs and specificities. They should focus on addressing the inherent difficulties of
modern policy making, which are caused by the complex and ‘wicked’ nature of
social problems (Rittel and Weber 1973; Kunz and Rittel 1979), enabling a better
and deeper understanding of the main elements of them (e.g. issues, alternatives,
advantages and disadvantages perceived by various stakeholder groups; Conklin and
Begeman 1989; Conklin 2003; Loukis and Wimmer 2012).
12.3 Research Method
The development of the two proposed approaches for active and passive crowd-
sourcing, respectively, was performed through close cooperation with public sector
employees experienced in public policy making, using both qualitative and quanti-
tative techniques: semi-structured focus group discussions, scenarios development
and questionnaire surveys.
12.3.1 Active Crowdsourcing
The development of our active crowdsourcing approach (described in ‘An Active
Crowdsourcing Approach’) included the following six phases (for more details on
them see DeliverableD2.1 ‘Padget Design and Decision Model for Policy Making’
of the PADGETS project accessible in its website www.padgets.eu):
a. Initially three semi-structured focus group discussions were conducted in the
three government agencies participating in the PADGETS project (mentioned in
the introductory section) as user partners (Center for eGovernance Development
(Slovenia), ICT Observatory (Greece), Piedmont Regional Government (Italy)),
which aimed at obtaining an understanding of their policy-making processes, the
degree and form of public participation in them, and also their needs for and
interest in ICT support.
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268 E. Loukis and Y. Charalabidis
b. The main themes of the above semi-structured focus group discussions were used
for the design of a questionnaire, which was filled in and returned to us through
e-mail by another four government agencies (City of Regensburg (Germany),
World Heritage Coordination (Germany), North Lincolnshire Council (UK), IT
Inkubator Ostbayern GmbH (Germany)), which have some form of close coop-
eration with the above three user partners of PADGETS project. This allowed us
to obtain the above information from a wider group of government agencies, and
cover a variety of government levels (national, regional and local).
c. Based on the information collected in the above first two phases the main idea
of the active crowdsourcing approach was formulated: combined use of multiple
social media for consultation with citizens on a social problem or public policy of
interest, and sophisticated processing of relevant content generated by citizens.
d. Three application scenarios were developed in cooperation with the above three
user partners of PADGETS project concerning the application of the above main
idea for a specific problem/policy of high interest. Each of these scenarios de-
scribed which social media should be used and how, what content should be
posted to them, and also how various types of citizens’ interactions with it (e.g.
views, likes, comments, retweets, etc.) should be monitored and exploited, and
what analytics would be useful to be computed from them.
e. Finally, a survey was conducted, using a shorter online questionnaire, concerning
the required functionality from an ICT tool supporting the use of social media
for such multiple social media consultation. It was distributed by personnel of
the three user partners involved in the PADGETS project to colleagues from
the same or other government agencies, who have working experience in public
policy making, and finally was filled in by 60 persons.
f. Based on the outcomes of the above phases C, D and E, we designed this govern-
ment active crowdsourcing approach in more detail, and then the required ICT
infrastructure and its application process model (described in ‘Description’, ‘ICT
Infrastructure’ and ‘Application Process Model’, respectively).
12.3.2 Passive Crowdsourcing
The development of our passive crowdsourcing approach (described in ‘A Passive
Crowdsourcing Approach’) included the following seven phases (for more details on
them see Deliverable D2.1 ‘Padget Report on User Requirements’ of the NOMAD
project in its website www.nomad-project.eu/):
1. Initially the main idea was developed, in cooperation with the user partners of the
NOMAD project (Greek Parliament, Austrian Parliament, European Academy
of Allergy and Clinical Immunology), based on the digital reputation and brand
management ideas from the private sector (e.g. see Ziegler and Skubacz 2006):
passive retrieval of content that has been generated by citizens freely (without
any initiation, stimulation or moderation through government postings) in nu-
merous Web 2.0 sources (e.g. blogs and microblogs, news sharing sites, online
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12 Active and Passive Crowdsourcing in Government 269
forums, etc.) on a specific topic, problem or public policy, and then sophisticated
processing of this content using opinion mining techniques.
2. Four application scenarios of this idea were developed by the above user partners
of the NOMAD project. Each application scenario constitutes a detailed realistic
example of how this passive croudsourcingidea could be applied for supporting
the formulation of a particular public policy, and describes how various types of
users involved in this might use an ICT platform that implements this idea.
3. A questionnaire was distributed electronically to a sample population of potential
users, which included questions concerning: (a) respondent’s personal informa-
tion, (b) general citizens’ participation information (in his/her organization), (c)
current use of social media in policy-making processes, (d) general assessment
of this ideaand and (e) specific relevant requirements.
4. Organization of focus groups and workshops with the participation of potential
users. This allowed in-depth discussion among people experienced in the design
of public policies, with different backgrounds and mentalities, about this new
idea, and also ways and processes of its practical application, required relevant
ICT functionalities and at the same time possible problems and barriers.
5. Organization of in-depth interviews based of a series of fixed questions concerning
attitudes towards this new idea, its usefulness and applicability.
6. A review of systems that offer at least a part of the above ICT functionalities (e.g.
for content retrieval, opinion mining, etc.).
7. Based on the outcomes of the above phases we designed this government passive
crowdsourcing approach in more detail, then its application process model and
finally the required ICT infrastructure (as described in ‘Description’, ‘Application
Process Model’ and ‘ICT Infrastructure’, respectively).
12.4 An Active Crowdsourcing Approach
12.4.1 Description
The proposed active crowdsourcing approach is based on the centralized automated
publishing of multimedia content (e.g. a short text, a longer description, images,
videos, etc.) concerning a social problem of interest or a public policy under formu-
lation to the accounts of a government agency in multiple social media (e.g. Facebook,
Twitter, YouTube, Picasa and Blogger), in order to actively stimulate discussions on
it. As mentioned in ‘Introduction’ and ‘Background’ social problems have become
highly complex and ‘wicked’, with multiple and heterogeneous stakeholders having
different problem views, values and objectives (Rittel and Weber 1973; Kunz and
Rittel 1979; Conklin 2003), so in order to address this inherent difficulty our method-
ology uses multiple social media, with each of them attracting different groups of
citizens. Throughout these social media consultations we continuously retrieve and
monitor various types of citizens’ interactions with the content we have posted (e.g.
views, likes, ratings, comments and retweets), and finally we process these interac-
tions in order to support drawing conclusions from them. Both content posting and
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270 E. Loukis and Y. Charalabidis
interactions’ continuous retrieval are performed in a highly automated manner using
the API of these social media from a central ICT platform, in which also processing
and results presentation takes place.
In particular, a government agency policy maker, through a web-based dashboard
or a mobile phone application, initiates a campaign concerning a specific topic,
problem or policy in multiple social media. For this purpose, he/she creates relevant
multimedia content (e.g. short and longer topic description, images, videos, etc.),
which are then automatically published in the corresponding social media (e.g. in
the Twitter the short-topic description, in Blogger the longer one, in YouTube the
video, in Picasa the images, etc.) by a central platform. The citizens will view this
content, and interact with it (in all the ways that each social media platform allows),
either through these social media, or through a mobile phone application. Then,
these interactions will be automatically retrieved and shown continuously to the
policy maker, through the above web-based dashboard or mobile phone application,
so that appropriate interventions can be made (i.e. new content can be published)
if necessary. Finally, after the end of the campaign, sophisticated processing of all
citizens’ interactions with the above content will be performed in this central ICT
platform, using a variety of techniques (e.g. calculation of web analytics and opinion
mining), in order to provide useful analytics that support government decision and
policy making. In Fig. 12.1, this active crowdsourcing approach is illustrated.
The practical application of the above approach will lead to a collection of large
amounts of content generated by citizens in various Web 2.0 social media concerning
the particular topic, problem or policy we have defined through our initial postings.
So it will be of critical importance to use highly sophisticated methods of automated
processing this content, in order to offer substantial support to government agencies
policy makers in drawing conclusions from it . Part of this citizens-generated content
is numeric (e.g. numbers of views, likes, retweets, comments, ratings, etc.), so it can
be used for the calculation of various analytics. However, a large part of this content
is in textual form, so opinion mining, defined as the advanced processing of text in
order to extract sentiments, feelings, opinions and emotions (for a review of them
see Maragoudakis et al. 2011), will be a critical technology for processing it and
maximizing knowledge extraction from it. The development and use of opinion
mining first started in the private sector, as firms wanted to analyze comments and
reviews about their products, which had been entered by their customers in various
websites, in order to draw conclusions as to whether customers like the specific
products or not (through sentiment analysis techniques), the particular features of
the products that have been commented (through issues extraction techniques) and
the orientations (positive, negative or neutral) of these comments (through sentiment
analysis techniques). These ideas can be applied in the public sector as well, since
citizens’ comments are a valuable source of information that can be quite useful
for government decision and policy making: it is important to identify the main
issues posed by citizens (through issues extraction) on a particular topic, problem
or policy making we are interested in, and also the corresponding sentiments or
feelings (positive, neutral or negative—through sentiment analysis). More details
about this active crowdsourcing approach are provided by Charalabidis and Loukis
(2012), Ferro et al. (2013) and Charalabidis et al. (2014a).
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12 Active and Passive Crowdsourcing in Government 271
Fig. 12.1 An approach for
active crowdsourcing in
government
12.4.2 ICT Infrastructure
An ICT platform has been developed for the practical application of the above ap-
proach, which provides all required functionalities to two main types of users of
it: government agencies’ policy makers and citizens. In particular, a ‘policy makers
dashboard’ (accessible through a web-based or a mobile interface (Android mobile
application)) enables government agencies’ policy makers:
1. To create a multiple social media campaign, by defining its topic, the starting and
ending date/time, the social media accounts to be used, and the relevant messages
and multimedia content to be posted to them
2. To monitor continuously citizens’ comments on the messages; in Fig. 12.2, we
can see this part of the web-based policy-makers’ interface, which is structured
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272 E. Loukis and Y. Charalabidis
Fig. 12.2 Policy-makers’ interface for viewing active campaigns, messages and citizens’ feedback
in three columns: in the first column, the active campaigns are shown, while by
selecting one of them in the second column are shown the corresponding messages
posted by the policy maker (the initial, and the subsequent ones), and finally by
selecting one of these messages in the third column are shown citizens’ comments
on it (textual feedback stream)
3. And after the end of the campaign to view (as graphics and visualizations) a
set of analytics and opinion mining results, which are produced by the decision
support component of the platform (described later in this section) for the whole
campaign.
The citizens can see the initial content of each campaign, and also other citizens’
interactions with it (e.g. textual comments), either through the interfaces of the cor-
responding social media, or through a mobile interface (Android mobile application)
or a widget, which enables citizens to view active campaigns, and by selecting one of
them to view all policy maker and citizens’ comments on it, or add a new comment.
The technological architecture of this ICT platform is shown in Fig. 12.3. We can
see that it consists of two main areas:
1. The front-end area, which provides the abovementioned web interface to the
policy makers, and also the mobile application and widget interfaces to both
policy makers and citizens.
2. The back-end area, which includes three components: the first of them perfoms
publishing of various content types in multiple social media through the second
component, which consists of connectors with the utilized social media, while
the third component performs aggregation/analysis of citizens interactions with
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12 Active and Passive Crowdsourcing in Government 273
Fig. 12.3 Active crowdsourcing ICT platform technological architecture
the above-published content in these social media, retrieved through the second
component; it consists of one subcomponent that allows continuous monitoring
of these citizens interactions, and several subcomponents that provide analytics
for government policy-makers’ decision support.
One of these subcomponents collects and processes the ‘raw analytics’ provided by
the analytics engines of the utilized social media. Another subcomponent provides
more advanced analytics, which concern citizens’ textual inputs (e.g. blog post-
ings, comments, opinions, etc.), processing them using opinion mining techniques
(Maragoudakis et al. 2011). In particular, it performs the following three types of
tasks:
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274 E. Loukis and Y. Charalabidis
• Classification of an opinionated text (e.g. a blog post) as expressing a posi-
tive, negative or neutral opinion (this is referred to as document-level sentiment
analysis).
• Classification of each sentence in a such a text, first as subjective or objective (i.e.
determination of whether it expresses an opinion or not), and for each subjective
sentence (i.e. expressing an opinion) classification as positive, negative or neutral
(this is known as sentence-level sentiment analysis).
• Extraction of specific issues commented by the author of a text, and for each issue
to identify its orientation as positive, negative or neutral (this is referred to as
feature-level sentiment analysis).
Another subcomponent performs simulation modelling (Charalabidis et al. 2011),
having mainly two objectives: estimation of the outcomes of various citizens’ propos-
als on the public policies under discussion, and also forecasting the future levels of
citizens’interest in and awareness of these policies. The simulation modelling takes as
input various indicators produced by the other two aforementioned subcomponents.
12.4.3 Application Process Model
Furthermore, an application process model for this active crowdsourcing approach
has been developed. It provides a model of the process to be followed by government
agencies for the practical application of it, which includes a sequence of specific
activities to be executed:
1. The policy maker initially setsup a policy campaign, using the capabilities of the
central ICT platform described above, through a graphical user interface
2. Then he/she creates textual content for this campaign (both short and longer policy
statements), and also can add various types of multimedia content to it (e.g. policy
images, video, etc.)
3. And finally defines the multiple social media accounts to be used in this campaign
4. And views a preview of the campaign in each of them
5. The campaign is launched by publishing the above content (in each of these
multiple social media will be automatically published the appropriate part of the
above content, e.g. in the Twitter will be published the short policy statement, in
Blogger the longer one, in YouTube the video, in Picasa the images, etc.).
6. Citizens interact with the published content in various ways in these social media
(in the particular ways each of them allows): they access and see this content, rate
it and make some comments on it, retransmit it in their networks, etc
7. The above citizens’ interactions are automatically retrieved continuously from
all the used social media in the central ICT platform, and after the end of the
campaign are processed there using various advanced techniques (as described
above), in order to calculate useful analytics that provide assistance and support
to the policy maker.
8. The results are sent immediately to the policy maker, by e-mail or SMS message.
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12 Active and Passive Crowdsourcing in Government 275
Fig. 12.4 A typical application scenario of the active crowdsourcing ICT approach
In Fig. 12.4, we can see a typical application scenario of this active crowdsourcing
approach.
12.5 A Passive Crowdsourcing Approach
12.5.1 Description
The proposed passive crowdsourcing approach is based on the exploitation of the
extensive political content created in multiple Web 2.0 sources (e.g. blogs and mi-
croblogs, news sharing sites, and online forums) by citizens freely (= without active
stimulation through some government posting) concerning various domains of gov-
ernment activity and public policies. An ICT platform automatically retrieves this
content from these Web 2.0 sources using their API, and then processes it using so-
phisticated linguistic processing techniques in order to extract from it relevant issues,
proposals and arguments. So in this approach government is not active in conduct-
ing crowdsourcing (as it is in the active crowdsourcing approach presented in the
previous section, by posing to citizens particular discussion topics, problems or poli-
cies), but it remains passive (just ‘listening’ to what citizens discuss, and analyzing
the content they freely produce in order to extract knowledge from it). Taking into
account the highly complex and ‘wicked’ nature of modern social problems, which
usually have multiple and heterogeneous stakeholders with different problem views,
values and objectives (Rittel and Weber 1973; Kunz and Rittel 1979; Conklin 2003),
our passive crowdsourcing approach uses multiple Web 2.0 content sources, with
diverse political perspectives and orientations.
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276 E. Loukis and Y. Charalabidis
In particular, this passive crowdsourcing approach includes three stages, which
are illustrated in Fig. 12.5. The first stage, called ‘Listen’, includes listening and mon-
itoring what citizens say concerning a domain of government activity (e.g. higher
education) or a public policy under formulation (e.g. a new policy on higher educa-
tion) in a large set of Web 2.0 sources S1, S2,. . . , SN defined by the policy maker.
For this purpose a ‘focused crawler’ is used, which is a program that browses the
above sources in an automated and organized manner, and retrieves solely content
that is relevant to the specific topic of interest.
The second stage, called ‘Analyse’, includes advanced processing and analysis
of the retrieved content, from which are identified relevant issues, proposals and
arguments expressed by citizens. As the majority of this content is in textual form,
this stage makes use of advanced linguistic processing techniques (for a review of
them, see Maragoudakis et al. (2011)). In particular, each content unit retrieved by
the crawler will go through a series of automated processing steps:
• Language detection, which will recognize the language used in it.
• Opinion and argument extraction, using appropriate semantic similarity measures
and inference mechanisms that allow the identification of elements of the analyzed
content which are pertinent to the particular domain or policy.
• Sentiment analysis, using smart sentiment classifiers that recognize the polarity
(positive, neutral, and negative) of the elements identified above.
• Argument summarization, using appropriate algorithms for generating qualitative
information about opposing arguments, in the form of anonymity-preserving and
automatically generated summaries.
The third stage, called ‘Receive’, aims to present to the end-user (policy maker)
the knowledge acquired from the previous stages in a complete, coherent and us-
able manner. The platform will provide an aggregated view of the results of the
above processing, their polarity, their association with various policy concepts and
statements, and also statistical indications of their significance and impact. For this
purpose visual analytics (Wong and Thomas 2004; Thomas and Cook 2005; Keim
et al. 2010) will be used, so that policy makers can view visualizations of the results
of previous stages, and easily understand them with minimal cognitive effort (e.g. in
a familiar word cloud form), which is quite important due to the high information
overload the policy makers usually experience.
The knowledge gained through this passive crowdsourcing (e.g. issues, propos-
als and arguments concerning a domain of government activity or a policy under
formulation) can be used in order to formulate more specific questions, positions or
proposals about the particular policy and then solicit citizens’ feedback and contribu-
tions on them through more ‘active’ forms of communication. This can be achieved
through ‘active crowdsourcing’, i.e. by making relevant stimulating postings (based
on the findings from passive crowdsourcing) to various social media (e.g. blogs,
Twitter, Facebook, YouTube, etc.), and also to official government e-participation
websites, in order to collect citizens’ interactions with this content (e.g. ratings,
votes, comments, etc.). Therefore, the proposed ‘passive crowdsourcing’ approach
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12 Active and Passive Crowdsourcing in Government 277
S1 S2 …. SN
Listen Analyse Receive
Fig. 12.5 The three stages of the government passive crowdsourcing approach
can be combined with the ‘active crowdsourcing’ approach described in the previ-
ous section, in order to increase its effectiveness. More details about this passive
crowdsourcing approach are provided by Charalabidis et al. (2014b).
12.5.2 Application Process Model
Extensive effort was required in order to design how the above passive crowdsourcing
concept can be practically applied by government agencies and work efficiently, and
formulate an apropriate process model for its application. So we will describe first
this aspect of it, and then the required ICT infrastructure in the following ‘ICT
Infrastructure’ (since the latter has been to a large extent based on the former). There
was wide agreement that since the domains of government activity and the public
policies for them are quite complex and multidimensional entities, it is not possible
to search for content on them in the predefined Web 2.0 sources using just a small
number of keyworks. So it was concluded that the best solution for addressing this
complexity is to develop a model of the specific domain, for which a policy is
intended, which will consist of the main terms of it and the relations among them (a
kind of ‘structured thesaurus’ of this domain). An example of such a domain model
for the energy domain, which has been developed based on the documents of the
‘Greek Strategy for Energy Planning’, is shown below in Fig. 12.6.
Based on such a domain model we can then build a policy model, by adding to
the nodes of the former: (a) the ‘policy statements’ (= the specific policy objectives
and actions/interventions that a policy includes) and also (b) positive and negative
arguments in favour or against them, respectively. An example of such a policy model
for the energy domain is shown in Fig. 12.7 (including three policy objectives, one
concerning the whole national energy planning, and two concerning the renewable
energy sources, six positive arguments and nine negative ones).
These two models (domain and policy ones) can be used for searching for and
retrieving relevant content concerning the main terms of a domain, or the policy
statements and the arguments of a policy. This search has to be performed at regular
time intervals in order to keep the retrieved content updated, and the results should
be stored in a database, and then undergo the advanced processing mentioned in the
previous section (in the ‘Analyse’ stage), the results of which will be also stored in
the same database. The authorized policy makers will have the capability at any time
to explore the results of this advanced processing stored in the above database, and
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278 E. Loukis and Y. Charalabidis
Fig. 12.6 Energy domain model
view various visualization of them, e.g. the most frequently mentioned terms-topics
with respect to a particular domain or policy model (e.g. in a tag cloud form).
Also, most of the potential users we interviewed mentioned that it is important
to view citizens’ sentiment with respect to these frequently mentioned terms-topics
(i.e. whether citizens regard each of them as positive, negative or neutral), or even
with respect to the individual policy statements and arguments of a policy model.
Furthermore, our interviewees noted that all the above (i.e. frequently mentioned
terms-topics and sentiments) may differ significantly between different citizens
groups (e.g. between age, gender, education and region groups), so policy mak-
ers should have the capability to view them for particular citizens’ groups, or to view
comparisons between different citizens’ groups. Furthermore, since public stance
changes rapidly, it was mentioned that policy makers should have the capability to
view all the above information for particular user-defined time periods, or to compare
between different time periods, while future forecasts of them would be quite useful.
Based on the above, a model of the process to be followed by government agencies
for the practical application of this passive crowdsourcing approach was developed.
It includes the following nine activities:
1. Development of a domain model
2. Development of a policy model
3. Definition of Web 2.0 content sources
4. Search of these content sources at regular time intervals
5. Process retrieved content and store results in a database
6. Policy maker views polarized tag glouds with the most frequently mentioned
terms-topics with respect to a particular domain or policy model and the
corresponding sentiments for a predefined time period.
7. Policy maker views the sentiments with respect to the individual policy statements
and arguments of a policy model.
8. Policy maker views the above for particular citizens’ groups, and then makes
comparisons between different citizens’ groups, or with other time periods.
9. Policy maker views short-term future forecasts of the above.
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12 Active and Passive Crowdsourcing in Government 279
Fig. 12.7 Energy policy model based on the above energy domain model (including policy
statements and arguments)
Finally, we identified four roles which are required for the practical application of
this process model:
• Domain models author: this role will create domain models and also modify
existing ones.
• Policy models author: this role will create policy models based on existing domain
models (= add to their nodes policy statements and argumentations) and also
modify existing ones.
• End user/policy maker: this role will view the results of processing the content
retrieved from the Web 2.0 sources in all the abovementioned forms.
• Platform administrator: this role will have full access to all platform functionali-
ties, monitor platform operation, manage the set of users accessing the platform
and their access rights to the offered services and functionalities.
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280 E. Loukis and Y. Charalabidis
12.5.3 ICT Infrastructure
Based on the above application process model, we proceeded to the design of the
functional architecture of the required ICT platform. In particular, we defined in
more detail the functionality to be provided to each of the above four roles:
1. Domain models author
– Creation of new domain models (= definition of main terms of the domain and
the relations among them).
– Modification of existing domain models.
– Import of external domain models (e.g. having the form of ontology files in
OWL).
– Export of domain models (e.g. in the form of ontology files in OWL).
2. Policy models author
– Access to domain models.
– Creation of new policy models (using existing domain models, by adding
policy statements and arguments to their nodes).
– Modification of existing policy models.
– Import of external policy models (e.g. having the form of ontology files in
OWL).
– Export of policy models (e.g. in the form of ontology files in OWL).
3. End user/policy maker
– View the most frequently mentioned terms-topics with respect to a particular
domain or policy model for a predefined time period, citizens’ group and
sources subset (see Fig. 12.8 for a first design of the corresponding screen).
– View sentiment for these terms-topics.
– View sentiment for each policy statement and argument of a particular model.
– View differentiations of the above over time.
– View differentiations of the above across citizens’ groups.
– View differentiations of the above across sources subsets.
– View short-term future projections of the above.
4. Platform administrator
– Users and roles management.
– Domain and policy roles management.
– Monitoring and administration of all platform services.
Based on the above functional architecture of the platform, its technological archi-
tecture was designed. The objective of this design was to provide this functionality
with an acceptable response time. Since this could not be achieved through online
retrieval of content from a large number of sources (e.g. numerous blogs, news web-
sites, Facebook, Youtube and Twitter accounts) and processing of it at the time a user
initiates a search, the only solution was to perform a scan of the predefined sources
at some regular time intervals (e.g. every 6 h) in order to retrieve new content, store it
in a database and then process it and store the results in the same database. Whenever
the user performs a search, the results will be produced in a very short time, using this
database. This separation between sources scanning and content processing on one
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12 Active and Passive Crowdsourcing in Government 281
Fig. 12.8 View of the most frequently mentioned terms-topics with respect to a particular domain
or policy model for a predefined time period, citizens’ group and sources subset
hand, and users’ searches processing on the other, allows a low response time and at
the same time sufficiently ‘fresh’ content for policy makers (i.e. allows addressing
these two conflicting requirements).
The above design leads to a three layers’ technological architecture of the platform,
which consists of a storage layer, a processing layer and a presentation layer, and
is shown in Fig. 12.9. Each of them includes a number of components, performing
different tasks, which act as services coordinated by an orchestration component.
In particular, the data storage layer includes the repositories where the raw and
processed content is stored:
• The content repository: it stores the raw content retrieved from the Web 2.0
sources, the cleaned content derived from the raw data, the content uploaded by
users and the results of the linguistic analysis associated with each content unit.
• The model repository: it stores in a structured form the domain and policy models
entered by users with domain expert and policy advisor roles.
• The metadata repository: it stores the metadata retrieved or calculated for our
sources.
• The thematic catalogues: it stores a representation of the thematic categories used
by the platform in order to characterize each content unit.
• The users repository: it contains information about the roles and the users of the
platform.
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282 E. Loukis and Y. Charalabidis
Presenta on Layer (User Interfaces)
Model AuthoringSystem Interface
Processing Layer
Data Acquisi on
Data Classifica on & Argument Summariza on
Keyword
Selec on
Rela on
Defini on
Argument
Building
Thema c
Classifier
Thema c
Catalogue
Dynamic Content
Crawlers
Sta c Content
Crawlers
Policy Model
Sharing
Visualisa on &
Analysis
Storage Layer
Thema c
Catalogues
Content
Domain/
Policy
Models
Content Cleaner
Metadata
Opinion Mining & Argument Extrac on
Sen ment
Analyser
Segment
Extractor
Argument
Extractor
Linguis c
Demographic
Extractor
Tag Cloud
Generator
Content Inser on
Argument
Summarizer
Users
Administra ve
Interface
Fig. 12.9 Passive crowdsourcing ICT platform technological architecture
The processing layer includes all the components that retrieve and process the content
from the predefined sources, which are organized in three sub-layers:
• The data acquisition layer, which includes the crawling components for fetching
content from the sources, using their APIs, as well as the modules responsible for
cleaning the fetched content and obtaining the actual textual information from it
(static content crawlers, dynamic content crawlers and content cleaner).
• The data classification and argument summarization layer, which includes (a)
the thematic classifier, which processes the available content and associates it
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12 Active and Passive Crowdsourcing in Government 283
with one more of the defined thematic categories in the thematic catalogues, and
(b) the result summarizer, which processes the available results and provides a
summarization that allows their presentation in a condensed manner.
• The argument extraction and opinion mining layer, which includes all the com-
ponents that process the available content and extract segments, arguments and
sentiments (segment extractor, argument extractor, sentiment analyzer, linguistic
demographic extractor, tag cloud generator).
The presentation layer includes all the components that either require input from the
user or present to him/her the results:
• The thematic catalogue interface, for entering or updating the available thematic
categories and also terms associated with each category.
• The keyword selection interface, which allows entering keywords/terms for
creating domain models.
• The relation definition interface, which allows the user to introduce relations
between the above keywords/terms for the definition of domain models.
• The argument building interface, which allows the user to insert in natural lan-
guage statements and arguments supporting or objecting to policy statements of
policy models.
• The policy model sharing interface, which provides a catalogue of the policy
models created by the user and allows defining them as visible to others.
• The admin interface, which provides the means to an administrator to manage the
configurable aspects of the system.
• The visualisation and analysis module, which utilizes the results of the processing
layer in order to provide the user with a view of domain and policy models, and
also various visualizations of the results of users’ searches, enabling also the
selection of sources, demographic characteristics and time periods.
The domain and policy modelling components of the presentation layer (thematic cat-
alogue, keyword selection, relation definition, argument building and policy model
sharing interfaces) will be based on the ELEON Ontology Authoring and Enrich-
ment Environment (http://www.iit.demokritos.gr/ eleon), developed by the National
Center for Scientific Research ‘Demokritos’, which participates as a partner in the
NOMAD project. It supports editing ontologies and relating such ontologies with lin-
guistic resources that can be used to extract structured ontological information from
text, and also supports the author with a number of innovative methods for ontology
checking (Bilidas et al. 2007) and autocompletion (Konstantopoulos et al. 2011).
The sentiment analyser will be based on existing tools developed by ‘Demokritos’
as well (Rentoumi et al. 2009; Rentoumi et al. 2010), which are based on algorithms
that take into account various intricacies of the language forms commonly used in the
context of user-generated web content, such as metaphors, nuances, irony, etc. For
the summarization task the ‘n-gram graph framework’ (Giannakopoulos et al. 2008;
Giannakopoulos and Karkaletsis 2009) will be used, which is a statistical, domain
agnostic and language-independent framework that allows the analysis of texts as
character n-gram graphs.
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284 E. Loukis and Y. Charalabidis
12.6 Comparisons
In this section, we make a comparison between the two proposed crowdsourcing ap-
proaches, and also with the private and public sector crowdsourcing patterns reported
in the literature (outlined in ‘Background’), identifying similarities and differences.
Both approaches adopt two of the four crowdsourcing approaches identified by
Brabham (2012): mainly ‘knowledge discovery’ and secondarily ‘creative produc-
tion’. From the four public sector specific crowdsourcing purposes identified by
Nam (2012) they focus mainly on ‘information creation’, and secondarily on ‘prob-
lem solving’ and ‘policy making advice’; also from the two types of collective
intelligence mentioned in the same study both approaches aim at ‘nonprofession-
als innovative ideas’ and much less at ‘professionals knowledge’. With respect to
participants’ motivation, from the two main motivation types identified by Rouse
(2010) both approaches are based mainly on citizens’ ‘community oriented’ motiva-
tions and much less on ‘individualistic’ ones (since none of the two approaches is
based on the monetary or other types of rewards used in private sector crowdsourc-
ing); also, from the seven more detailed participants’ motivations identified in the
same study the ‘altruism’, ‘instrumental motivation’ and ‘social status’ seem to be
ones our approaches mainly rely on. Finally from the four organizer benefits identi-
fied in the same study, both methodologies aim to provide to adopting government
agencies ‘access to capabilities not held in-house’ and ‘capacity to exploit knowledge
and skills of volunteers who might not otherwise contribute’, but not ‘cost savings’
or ‘contracts and payments that are outcome based’.
With respect to the required ICT infrastructures it should be noted that the one of
our active crowdsourcing approach—described in ‘ICT Infrastructure’—has some
similarities with the typical crowdsourcing IS (which according to Hetmank (2013))
includes user, task, contribution and workflow management components), but also
important differences as well. In particular, this active crowdsourcing ICT platform
includes ‘task management’ components (that enable setting-up a campaign and cre-
ating/adding multimedia content to it) and ‘contribution management’ components
(processing citizens’ interactions with the above content in the utilized social media).
However, it does not include ‘user management’ components (as the management
of the citizens participating in our campaigns is conducted through our social me-
dia accounts) and ‘workflow management’ ones. Also the process model we have
developed for the application of this active crowdsourcing approach—described in
‘Application Process Model’—has some similarities with the typical crowdsourcing
process model (according to Hetmank (2013)), but also important differences as well.
In particular, this application process model includes four out of the ten activities of
this typical crowdsourcing process model (define task, set time period, accept crowd
contributions, and combine submissions), however most of them in a quite different
form. However, the former does not include the remaining six activities of the latter
(state reward, recruit participants, assign tasks, select solution, evaluate submissions
and finally grant rewards), due to inherent differences of our active crowdsourcing
approach from the mainstream crowdsourcing (e.g. lack of reward and specific task
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12 Active and Passive Crowdsourcing in Government 285
assignments, participants management through our accounts in the utilized social
media, lack of individual submissions evaluation, etc.).
On the contrary, both the application process model of our passive crowdsourcing
approach and also the structure and components of the required ICT platform are
quite different from the one of the typical crowdsourcing approaches, which has been
identified by Hetmank (2013). In particular, our passive crowdsourcing approach
does not include any of the main tasks of the mainstream crowdsourcing (problem
definition, open call for contributions, search for and motivation of contributors,
evaluation of contributions, and finally reward of the most successful of them), but
has a quite different task structure (including domain and policy modelling, definition
of the Web 2.0 sources to be used, automated content retrieval and sophisticated
processing of the retrieved content, which do not exist in mainstream crowdsourcing).
For this reason, its application process model—described in 5.2—is quite different
from the one of the typical crowdsourcing. Also, the passive crowdsourcing ICT
platform we have designed—described in 5.3—includes ‘contribution management’
components (allowing advanced linguistic processing of the textual content retrieved
from multiple Web 2.0 sources), but not ‘task management’, ‘user management’ and
‘workflow management’ ones. This new passive crowdsourcing approach requires
more extensive and complex ICT infrastructures than the existing crowdsourcing
approaches, which are based on the use of API of numerous Web 2.0 sources, in
combination with advanced linguistic processing techniques.
12.7 Conclusions
Crowdsourcing has been initially developed and applied in the private sector, and later
introduced in the public sector (still in experimental mode). Therefore, there is limited
knowledge concerning the efficient and effective application of crowdsourcing ideas
in government, taking into account its special needs and specificities, much less
than in the private sector. This chapter contributes to filling this gap, presenting two
approaches for this purpose: a first one for ‘active crowdsourcing’, and a second one
for ‘passive crowdsourcing’ by government agencies. The foundations of both come
from management sciences (crowdsourcing research), political sciences (wicked
social problems research) and technological sciences (social media capabilities and
API). For each of these approaches has been presented the basic idea, the architecture
of the required ICT infrastructure, and its application process model.
A common characteristic of the two proposed government crowdsourcing ap-
proaches is that they do not include competitive contest among the participants and
monetary or other types of rewards, as in private sector crowdsourcing, but mainly
collaboration among citizens for knowledge and innovative ideas creation. Also they
both rely mainly on community-oriented motivations of the participants and not
on individualistic ones. They aim to provide to adopting government agencies not
benefits associated with ‘cost savings’ or ‘contracts and payments that are outcome
based’ (as in the mainstream private sector crowdsourcing), but benefits concerning
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286 E. Loukis and Y. Charalabidis
Table 12.1 Similarities and differences between the proposed active and passive crowdsourcing
approaches
Similarities
Both approaches exploit multiple Web 2.0 social media simultaneously
In a centrally managed manner based on a central platform
Fully automatically using their API
And then both make sophisticated processing of the collected content, in order to extract the main
points from it, in order to reduce the ‘information overload’ of government decision makers
They both aim to provide to government agencies access to resources (e.g. information, knowledge,
ideas, and skills) not available in-house
But without competitive contests and monetary rewards (which are quite usual in private sector
crowdsourcing)
Relying both on community oriented motivations of the participants and not on individualistic
ones
Differences
The active crowdsourcing approach uses the accounts of the particular government agency in
several social, while the passive crowdsourcing approach goes beyond them, using other accounts,
blogs, websites, etc., not belonging to government agencies
Also the former actively stimulates discussions and content generation by citizens on specific topics
(through government postings and content), while the latter does not: it passively collects content
created by citizens freely, without any initiation, stimulation or moderation through government
postings
The initial preparation—content generation requirements for the application of the passive crowd-
sourcing approach (= creation of domain and policy models) are much higher than the ones of
active crowdsourcing
The processing of the collected content has to undergo much more sophisticated processing in the
case of the passive crowdsourcing approach than in the active crowdsourcing one
And also the required ICT infrastructure for the active crowdsourcing approach, and its application
model are more similar to the ones of the mainstream private sector crowdsourcing than the passive
crowdsourcing approach
‘access to capabilities not held in-house’ and ‘capacity to exploit knowledge and
skills of volunteers who might not otherwise contribute’. However, while for our
active crowdsourcing approach the required ICT infrastructure and its application
process model have some similarities with the ones of the mainstream private sector
crowdsourcing (also important differences as well), our passive crowdsourcing ap-
proach requires quite different forms of ICT infrastructure and application process
model from the ones of the mainstream crowdsourcing. The similarities and differ-
ences between the two proposed approaches are summarized below in Table 12.1.
However, it should be noted that these two approaches are not mutually exclusive,
but can be combined: the results of passive crowdsourcing can be used for guiding
active crowdsourcing on the most important of the identified issues and problems,
or even for organizing relevant discussions in government e-consultation spaces.
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12 Active and Passive Crowdsourcing in Government 287
From a first evaluation we have conducted for the active crowdsourcing approach
based on pilot applications (see Ferro et al. 2013; Charalabidis et al. 2014a), it has
been concluded that it constitutes a time and cost efficient mechanism of reaching
wide and diverse audiences, and stimulating and motivating them to think about
social problems and public policies under formulation, and provide relevant infor-
mation, knowledge, ideas and opinions. Furthermore, it enables identifying the main
issues perceived by citizens with respect to a particular social problem or domain
of government activity, and collecting from them interesting ideas on possible so-
lutions and directions of government activity. However, our pilot applications have
shown that the above information generated from such multiple social media crowd-
sourcing might be not be at the level of depth and detail required by government
agencies. So in order to achieve a higher level of detail, and more discussion depth
in general, a series of such multiple social media consultations might be required,
each of them focused on particular subtopics and/or participants. Another risk of this
active crowdsourcing approach is that it can lead to unproductive discussions among
like-minded individuals belonging to the network of the government policy maker
who initiated the consultation; such discussions are characterized by low diversity of
opinions and perspectives, low productivity of knowledge and ideas, and in general
limited creativity. Therefore, for the effective application of this crowdsourcing ap-
proach it is of critical importance to build large and diverse networks for these social
media consultations; for his purpose, we can combine networks of several govern-
ment agencies, and also politicians, preferably from different political parties and
orientations, and also invite additional interested and knowledgeable individuals and
civil society organizations. Our passive crowdsourcing approach is currently under
evaluation based on pilot applications.
The research presented in this chapter has interesting implications for research and
practice. It opens up new directions of multidisciplinary research concerning the ap-
plication of crowdsourcing ideas in government, taking into account its special needs
and specificities, and also for the development of advanced ICT infrastructures for
this purpose, and appropriate application process models. With respect to govern-
ment practice, it provides to government agencies advanced, efficient and effective
methods and ICT tools, in order to conduct ‘citizen sourcing’, and collect useful
information, knowledge, ideas and opinions from citizen, and the society in general,
so that it can finally design better, more socially rooted, balanced and realistic public
policies for addressing the growing problems of modern societies. Such tools can be
for government policy makers valuable ‘sensors’, allowing the early identification
of new problems, needs, ideas and trends in the society, so that appropriate policy
responses can be developed. It is important that such approaches are gradually intro-
duced and integrated in the policy formulation processes and practices, which can
lead to a significant ‘renewal’ of them.
Further research is required concerning the multidimensional evaluation of the
two proposed government crowdsourcing methodologies, through various ‘real-life’
applications (aiming at conducting crowdsourcing for various types of problems and
public policies), and using various theoretical foundations and lenses from multiple
w.jager@rug.nl

288 E. Loukis and Y. Charalabidis
disciplines. Also, it would be interesting to conduct research towards the develop-
ment of contest oriented government crowdsourcing methodologies, which include
definition of a more specific task to be performed, competition among participants
and monetary or other types of rewards.
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w.jager@rug.nl

Chapter 13
Management of Complex Systems: Toward
Agent-Based Gaming for Policy
Wander Jager and Gerben van der Vegt
Abstract In this chapter, we discuss the implications of complexities in societal sys-
tems for management. After discussing some essential features of complex systems,
we discuss the current focus of managers and management theory on prediction and
the problems arising from this perspective. A short overview is given of the leadership
and management literature, identifying what information is lacking concerning the
management of complex systems. Next agent-based gaming, which allows for model-
ing a virtual and autonomous population in a computer-game setting, is introduced as
a tool to explore the possibilities to manage complex systems. The chapter concludes
with a research agenda for management and leadership in complex systems.
13.1 Introduction
The Dexia bank run, which started with a tweet, and Project X Haren, that started
with an open invitation on Facebook, demonstrate that social interactions may give
rise to developments that spin out of control. In many different areas, managers in
both the public and private sectors have to deal with the management of such com-
plex behaving systems, e.g., the transition in the energy system, the development
toward sustainability of our society, the developments in our health care system and
the robustness of our financial–economic system, to name a few. Complexity the-
ory applied to social systems contributes to our understanding of the mechanisms
driving the sometimes turbulent developments in such social systems. It explains
how the interactions between many individual agents may result in sometimes sur-
prising processes of self-organization. In Chap. 4, Jager and Edmonds explained
the principles of social complexity in more detail. A relevant contribution of the so-
cial complexity perspective is that it explains under what conditions a social system
is rather predictable, and under what conditions it may start behaving turbulently,
W. Jager (�)
Groningen Center of Social Complexity Studies, University of Groningen, Groningen,
The Netherlands
e-mail: w.jager@rug.nl
G. van der Vegt
Faculty of Economics and Business, University of Groningen, Groningen, The Netherlands
© Springer International Publishing Switzerland 2015 291
M. Janssen et al. (eds.), Policy Practice and Digital Science,
Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_13
w.jager@rug.nl

292 W. Jager and G. van der Vegt
making prediction in a classical sense impossible. For example, the car market has
been a relatively predictable market for many years. For many brands and models,
estimations of sales were being made that often lived up to their expectations. How-
ever, the introduction of hybrid and electric cars in the existing market resulted in
turbulences. One example would be that in 2013 virtually all produced Mitsubishi
Outlander PHEV (plug-in hybrid electric vehicle) models were shipped to the Nether-
lands due to a beneficial financial regime in that country. In 2014, the sales of this
model dropped significantly1. It can be imagined that with the introduction of newer,
more radical designs, such as the Google driverless car and new systems of car-
sharing that utilize web based sharing tools new uncertainties are introduced in the
car-market that may give rise to large turbulences and unpredictability in the market.
In a way this reflects the uncertainties of a century ago when steam and gasoline
were two viable and competing sources of propulsion.
Besides technological aspects, social aspects are also critical in the success or
failure of introducing a new product or technology. Uncertainty, social norms, the
spreading of rumors through social networks, and the behavior and opinions of role
models all have significant effects on the success or failure and contribute to the
turbulence during the introduction of new technology. Although social mechanisms
underlying social complex phenomena have been identified in many social–scientific
studies, the management of developments in turbulent systems remains problematic.
At the same time, the more social interaction takes place in a system, which is usually
the case with the introduction of radical new technology, the more turbulently it can
behave, and the more important effective management of the system becomes. Yet
little is known about the effective management of complex systems’ behavior. It is
precisely in such turbulent situations where good management can result in favorable
outcomes. However, bad management may result in disasters hitting the news, such
as failed evacuation plans, civil war, or power shortages. Hence, the question—if we
can develop a tool to identify managerial leadership styles that help better manage
complex social systems—seems a highly relevant one.
13.2 Simulating Social Complex Phenomena
In improving our understanding of how social complex systems can be managed, the
first step is getting a better understanding of how interactions between people may
give rise to social complex phenomena. Due to the large scale of many social systems
and the often unique events that happen, experimentation with real populations is not
possible. However, it is possible to experiment with computer simulated populations
of artificial people, through so-called agent-based modeling (ABM, see e.g., Chap. 4).
This methodology has proven to be a suitable approach in exploring the dynamics of
social complex systems and is gaining momentum in many disciplines (e.g., Gilbert
1 In December 2013 4988 Mitsubishi Outlander PHEV were registered, against 83 in January 2014
(Kane 2014).
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13 Management of Complex Systems: Toward Agent-Based Gaming for Policy 293
and Troitzsch 2005). In ABMs, agents are connected in a network and follow simple
rules that are programmed at the individual level. In a model to investigate a particular
social system, these rules can be derived from a more general behavioral theory as
well as specific data originating from the field.
An example is the model of Van Eck et al. (2011), where the role of opinion
leaders on the diffusion of a new product was explored. In an empirical study, they
found that opinion leaders had a more central network position, possess more ac-
curate knowledge about a product, and tend to be less susceptible to norms and
more innovative. Implementing this in an agent-based model opened the possibility
of introducing new products in a simulated market and comparing the effects of the
presence versus absence of such opinion leaders in the system. The simulation experi-
ments demonstrated that opinion leaders increase the speed of the information stream
and the adoption process itself. Furthermore, they increase the maximum adoption
percentage. The simulation model thus suggests that targeting these opinion leaders
might be a viable marketing strategy.
ABM makes it possible to conduct many experiments and explore the conditions
under which social systems start behaving turbulent, which implies that the social
system gets into a state where fast and unforeseen developments take place, such
as in fashion dynamics or social conflicts. ABM also allows for exploring how
individuals change their behavior over time due to social interactions and allows
for the identification of the key individuals in a social network. Interestingly, it
opens the possibility to explore how certain management strategies would perform
in different conditions of turbulence.
13.3 Managing Social Complex Phenomena
From a social scientific experimental perspective, one would suggest running exper-
imental designs as to identify the effects of different strategies. Comparing different
interventions in a simulation model would yield information on what interventions
are most effective. However, whereas empirically validated ABMs clearly provide a
relevant perspective on identifying the social complexities in many social systems,
their application in experimentally testing the effects of operational management
strategies remains problematic in turbulent conditions. Two key reasons cause that
experimentation with predefined leadership interventions are problematic.
First, in a turbulent system the effects of a specific management strategy may
vary considerably. This is because in one simulation run such a strategy may be
on spot with the developments that take place in the simulation, whereas in another
simulation run the same timing of that specific strategy may be very inconvenient. As
a result, specific management strategies may have different consequences, making
it difficult to draw conclusions about their effectiveness.
Relaxing the experimental rigor of adopting certain management strategies at an
identical moment in the simulation would allow for tracking the developments in the
simulation and adopting the strategy at a moment that seems most effective. This
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294 W. Jager and G. van der Vegt
implies that the experimentation bears an adaptive character, responding with the
manipulation on developments in the simulation run as they evolve. However, here we
run into the problem that in understanding effective management of a system it is not
sufficient to study specific leadership behaviors in isolation. Rather, the management
of social complex phenomena often implies that a sequence of behaviors takes place,
and that (unforeseen) responses of other stakeholders have to be addressed as well.
In regular experimental designs as used in the social sciences, this would result in an
exponential growth of possible strategies to be tested. For example, given a simulated
simple market with ten competing products, a manager trying to stimulate the sales
of an “innovative green” product may decide on pricing, quality of the product, and
type of marketing. Each of these elements already implies a choice from a wide
array of possibilities. In selecting one possibility, the other product managers have
an equal number of possibilities that even interact with each other. Many decision-
making contexts are much more complex than this example, as they involve many
stakeholders with different and sometimes conflicting goals, different valuations, and
perspectives on outcomes, and different responsibilities and influencing power. And
realizing that many complex social processes may span longer periods of time (e.g.,
years), it is clear that testing the effect of particular strategies in managing turbulent
behaving social systems is not feasible in ABMs.
However, on a more aggregate level we may identify consistent patterns in man-
agerial behavior, such as being adaptive to change, collecting information, and having
a long-term perspective. These can be understood as a management or leadership
style, and we hypothesize that these styles are far less divergent than operational man-
agement strategies, so that their effectiveness may be observed from the interaction
between managers and a complex social system.
13.4 Leadership and Management in Complex Systems
The dominant paradigm in leadership research has been to examine the relation-
ships between leadership styles, such as task- and relationship-oriented behavior
(Bass 1990), and the outcomes of these behaviors, including follower attitudes (sat-
isfaction, commitment, trust), behaviors (extra effort, cooperation, organizational
citizenship behavior), and performance or unit level outcomes, like group cohesion,
collective efficacy, and unit performance. Within this paradigm, the vast majority of
studies has examined such relationships at a single period in time and has ignored the
dynamic character of most of these relationships. In the field of leadership studies,
this state of affairs is unfortunate because: (1) the effectiveness of specific leader-
ship behaviors may depend on their timing, and (2) because leadership essentially
represents a dynamic influence process between leader and followers that unfolds
over time (Uhl-Bien and Marion 2009). Scholars have, therefore, recently started to
develop theoretical frameworks that address these shortcomings, and now focused
on team leadership as a dynamic process necessitating adaptive changes in leader
behavior.
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13 Management of Complex Systems: Toward Agent-Based Gaming for Policy 295
Kozlowski et al. (2008) proposed that the effects of certain leadership styles and
actions may depend on their timing. They developed a team leadership framework
that portrays team development as a cyclical and dynamic process, which requires
leaders to adapt their leadership style to the various phases of team development
and to the different adaptation needs of the team at each phase. This means that
certain leadership actions and interventions may lead to desirable outcomes at a
certain phase of the relationship but not in another phase. The theoretical framework
proposes that for leaders to be adaptive, they must be aware of the key contingencies
that necessitate shifts in leadership behavior, and they must possess the underlying
skills needed to help the team resolve challenges. In these models, the leader has two
major responsibilities or functions.
One leadership function is instructional and regulatory in nature. By responding to
variations in team tasks by goal setting, performance monitoring, diagnosis, and feed-
back, the leader may help or stimulate team members to develop the knowledge and
skills that contribute to team effectiveness. Leadership behaviors associated with this
leadership function are transactional, structure-initiating, monitoring, authoritative,
and directive leadership.
The second leadership function is developmental. As teams acquire the necessary
knowledge and skills, the leader role shifts to help the team develop progressively
more complex skills and capabilities (Kozlowski et al. 2008). Leadership behav-
iors associated with this leadership function are transformational, consideration,
coaching, empowerment, facilitative, and participative leadership. Over time, this
dual-pronged leadership process is hypothesized to yield team-level regulation and
adaptive teams.
Other scholars have developed theoretical frameworks that explicitly conceptual-
ize leadership as a dynamic influence process between leaders and followers. They
suggest that leadership has to be understood as a reciprocal interaction process be-
tween leaders and followers, taking into consideration the characteristics, actions,
and reactions of both sides (Collinson 2005). Uhl-Bien et al. (2007), for example,
have proposed complexity leadership theory (CLT) as a model of team leadership
consistent with this line of thinking. According to CLT, the question is how leaders
might enable and coordinate the dynamic interactions between interdependent in-
dividuals without suppressing their adaptive and creative capacity. It distinguishes
between three leadership functions that are important in this regard: adaptive, ad-
ministrative, and enabling leadership. Adaptive leadership refers to the creative and
learning actions that emerge from the interactions between team members; it is an
informal, emergent dynamic that occurs among interacting individuals. Adminis-
trative leadership refers to the actions of persons in formal managerial roles who
plan and coordinate activities to accomplish organizationally prescribed outcomes
in an efficient and effective manner; it is about structuring tasks, planning, building
a vision, allocating resources, and managing crises. Enabling leadership is behav-
ior aimed at creating appropriate conditions to foster effective adaptive leadership
in places where innovation and adaptability are needed, and facilitating the flow
of knowledge and creativity from adaptive structures into administrative structures.
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296 W. Jager and G. van der Vegt
Enabling leadership tailors the behaviors of administrative and adaptive leadership
so that they can effectively function in tandem with one another.
Although leadership scholars have acknowledged the dynamic nature of leader-
ship and have started to develop models and frameworks to organize and describe
the factors that are important, empirical research testing these models is scarce. The
reason for the lack of research on dynamic leadership processes is that it requires
a different approach than the traditional research methods employed in leadership
research. Even multiwave studies are at best a series of time-spaced sequential snap-
shots and do not capture the dynamic relationships between inputs and outcomes over
time. Serious gaming may provide a suitable tool to enable real-time observation of
leadership processes: longitudinal methods for the real-time tracking of leadership
relationships and consequences as they evolve over time.
13.5 Serious Gaming
Serious gaming is well equipped as a tool to experiment with and train people for
situations that are less easy to practice in reality (e.g., Lisk et al. 2012). The flight
simulator is the most renowned serious game in this context, offering the possibility
to develop and maintain the skills to deal with all kinds of flying events, starting
with routine flights and ending with co-occurrences of rare events that in reality
often lead to disaster. One of the main advantages in studying and training behavior
is that the behavior of a person can be observed and tracked over time, offering
precise and controlled measurements, and allowing for detailed evaluations of the
behavior. Currently gaming is widely adopted in military training, and increasingly
being used in testing, for example, the organization of firefighting departments, new
product introductions (marketing), the optimal crowd streams in cities and stadiums,
and managing traffic flows. Leaving the implementation of strategies to real people
interacting with the model thus has the advantage of creating a plausible managerial
environment, as real people manage the system.
Up till now, serious games that are being used to study and train leadership and
management were based on deterministic models. The basic principle here is that
the player is confronted with a choice, and depending on the selection, a predefined
scenario path is picked. Whereas the multitude of choices creates a large landscape
of possible routes through the game, the game is deterministic by nature. This im-
plies that current games are not capable of capturing the social complex phenomena
mentioned earlier that emerge as the result of many autonomous interacting agents.
Yet for testing leadership and management in social complex systems over time, it
is critical to include these social complexities in a gaming setting. Whereas several
scientists within the ABM community identified gaming as a tool to explore and fore-
cast developments in complex systems (e.g., Arai et al. 2006; Guyot and Honiden
2006), it has not been used to study leadership and management of social complex
systems. Considering that especially turbulent developments in social systems can
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13 Management of Complex Systems: Toward Agent-Based Gaming for Policy 297
be problematic and require effective leadership, and observing that management sci-
ence lacks a suitable tool to study this, we propose to use agent-based gaming as a
new tool to study leadership and management behavior of real people in a controlled
simulated complex environment.
13.6 Agent-Based Games for Testing Leadership and
Management
To study the management of social complex phenomena we propose developing
agent-based games that includes an autonomously behaving artificial population.
Players will perform a management task in this game, allowing us to experimentally
test under what system characteristics a specific management strategy or leadership
style performs best. We hypothesize that in systems that are more turbulent, an adap-
tive and people-oriented style will outperform conservative and outcome-oriented
styles. More adaptivity prevents unwanted lock-ins to emerge, and a people-oriented
style will translate in better relations, and thus performance in the long run. However,
we also hypothesize that a long-term goal orientation is critical in efficient leader-
ship/management. Just concentrating on adapting to short-term developments may
cause that the long-term goals are neglected, which may also be a sign of ineffective
management. Hence, our basic hypothesis is that effective management of social
complex systems requires a strong adaptive capacity for responding to short-term
developments combined with a clear long-term perspective of the goals to reach. For
example, in stimulating the adoption of more sustainable technologies, one should
be very alert on what is currently happening in the market, and responding to threats
and identifying opportunities, but at the same time one should have a clear vision of
how the successful diffusion of sustainable technology creates future conditions for
further developments.
Currently, several practice-oriented agent-based simulation models have been de-
veloped that are aimed to support policy making. A recent inventory initiated by
Gilbert2 demonstrated that dozens of simulation models are being used in practical
settings. In many instances, the contributions remain at a more conceptual level,
providing the practitioners with a deeper understanding of the complexities of the
system they are interacting with, and the implications for policy making. If prac-
titioners actually interact with a model it usually requires close guidance by the
modelers because of the expertise required to interact with the interface.
It is our stand that the use of social simulation models, and their application
in studies exploring managerial and leadership styles, is necessary to work toward
interfaces that make it easier and more intuitive to interact with the simulation tool.
In the gaming industry, many developments took place, and as a result there is an
2 On 04-12-2013, Gilbert sent out an e-mail on the SIMSOC list asking for examples of ABMs that
have actually been used to support policy decision making or for other purposes “in the real world.”
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298 W. Jager and G. van der Vegt
abundance of games that create a very convincing environment in which it is relatively
easy to operate, to learn what the behavioral options are, and most importantly, to
create an experience of “flow” in the players that keeps them motivated to continue
playing the game. Important here is that the behavior of simulated agents and the
environment one interacts with provide a sufficiently realistic experience.
Whereas for amusement games convincing graphics are essential to create a flow,
and we emphasize the importance of developing high-quality interfaces for agent-
based games, the applicability of appeal of these serious games resides primarily in
the realism of the behavioral dynamics that are being simulated. This implies that
the behaviors as displayed by the artificial agents should reflect the behavior that
can be observed in reality. The consumat approach has specifically been designed
to capture a number of main processes of human behavior and has been used in a
variety of applications (see e.g., Jager 2000; Jager et al. 2000). Figure 13.1 gives an
overview of the components in the consumat framework.
A simulated agent, when satisfied and certain, may continue repeating itself many
times. Only if the satisfaction drops, and/or the uncertainty rises, the consumat
may switch to using a different decisional strategy. If this results in an increase in
satisfaction and the uncertainty is reduced, the agent may return to a new habitual
behavior.
Agents differ concerning their uncertainty tolerance (when to engage in social
processing) and aspiration level (when is an agent dissatisfied). The agents also
differ concerning the relative importance of the needs that determine their satisfaction
(individual preferences). As a consequence, an action directed on a specific agent
may result in different responses depending on the type and state of an agent. Agents
also have different abilities, such as financial means, which determine if a behavior
is possible to perform.
The consumat approach has been used to guide the modeling of different agent-
based models, such as fashion dynamics (Janssen and Jager 2001), transitions in
a society (Jager et al. 2000), sustainable life styles (Bravo et al. 2013), farmer
crop choice behavior (Mialhe et al. 2012; Speelman 2014), and currently projects
are addressing the diffusion of light-emitting diode (LED) household lighting
(Schoenmacker 2014) and electric cars (Jager et al. 2014).
A key attribute of the artificial population in our proposed agent-based gaming
approach is that the agents are linked in a social network, where some agents have
more links and influence than other agents. This implies that experiences of agents
are being communicated through the social network as information exchange and
normative influences, and may give rise to the emergence of precisely those social
complexities and turbulences where we want to study management and leadership.
Depending on the requirements of the topic to be modeled, one can implement static
or dynamic networks (see e.g., Squazzoni et al. 2013). For example, in developing
a game directed at the diffusion of new energy technology one could use a static
network with a number of actors having many contacts, representing influential
opinion leaders. However, in developing a model to experiment with the emergence
of extremism it is essential to include dynamic networks that allow for clusters of
agents that separate from mainstream society.
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13 Management of Complex Systems: Toward Agent-Based Gaming for Policy 299
optimising
inquiring
repetition
imitation
Fig. 13.1 How different factors influence one another and result in behavior (opportunity consump-
tion), which aggregates over all simulated consumers and results in macrolevel outcomes that set the
conditions for a next behavioral cycle. In the consumat approach, the agents have existence needs
(e.g., food, income), social needs (group belongingness and status), and identity needs (personal
preferences, taste). To select a behavior an agent can employ four different types of decisional
strategies, depending on its satisfaction and uncertainty. A satisfied and certain agent will repeat its
previous demand, which captures habitual behavior/routine maintenance. A satisfied but uncertain
agent will imitate the demand of a similar other in its network, which reflects normative compli-
ance (fashion). A dissatisfied and certain agent will evaluate all possible demands and select the
one providing the best outcomes (optimizing). And finally, a dissatisfied and uncertain agent will
inquire the demands other agents had and copy this demand if the outcomes are expected to be
better (social learning).
13.7 Single and Multiplayer Settings
A player confronted with an agent-based game using this approach will experience
realistic behavioral dynamics. First, because the consequences and impacts of policy
decisions will be communicated through the network of agents, which may elicit
far fetching effects, treating one person wrong may elicit a cascade of negative
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300 W. Jager and G. van der Vegt
information, whereas convincing an opinion leader of the benefits of, e.g., an energy-
efficient device may stimulate the social diffusion of this device. Players thus learn
how their policies may spread through the social system and may develop strategies
that use these social forces. This also implies that a player becomes aware of the fact
that his/her policy action may spread through society and hence may have long-term
effects on other agents beyond those directly affected in the here and now.
A second valuable outcome of using such a model is that the player not only
observes the impact of policy measures in the diffusion of products of the changing
of behavior but also sees what is happening with the level of need satisfaction of
different groups of agents. Hence, a deeper insight is generated concerning the effects
of policy on well-being and it can be observed if well-being drops, so measures can
be taken before a decrease in well-being results in negative behavior.
Up till now, we mentioned the players as an individual interacting with the sim-
ulation model. And indeed it is very well possible to let an individual interact with
the simulation tool, both in training, teaching, and experimentation settings. How-
ever, many social complex systems are being managed by multiple stakeholders. In
developing agent-based models several researchers already include different stake-
holders in the model building stages and also in the playing of the game, which were
often cardboard games (e.g., Barreteau et al. 2001). In the context of gaming, it
is already a common practice to use multiplayer settings. An example is World of
Warcraft (WoW), an Internet-based war game that started in 1994 as a simple com-
puter game and transformed into a massively multiplayer online role-playing game
(MMORPG), where over 7 million people are playing. A very relevant attribute of
WoW that makes it very appealing to play is the possibility to join a team of people
in accomplishing a mission. Here similarities in interest and complementary in skills
may lead to effective teams. These teams are often in competition with other teams
and often conflicts arise, which are being settled in fights.
In managing social complex systems often the same social structures can be
observed as in WoW and similar games. Decisions by a firm or organization are
made by groups of people having different and often complementary competences,
and they often have other and sometimes conflicting interests than other companies
and organizations. For agent-based gaming, a same venue can be created as in such
games. Instead of single players, a team may be composed, and it can be studied what
team compositions function well in managing turbulent situations in social complex
systems. It depends on the issue at stake if multiple teams are a relevant extension
of the agent-based game.
An example of a single group agent-based game would be a crowd managing game.
Here an artificial crowd can be modeled (see e.g., Wijermans et al. 2013), which is
gathering at a specific environment, such as a city center or a stadium. The crowd
can be given a general motivation, e.g., demonstrating, watching a performance, of
attending a soccer match, and a distribution can be given of the behavioral tendencies
of the simulated agents, e.g., in terms of aggression motivation. The players can be
a police task force that is composed of people in the field and a command level.
At the field level, a number of players will represent law-enforcement in the field.
These players will move in the environment and have a perception of the local
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13 Management of Complex Systems: Toward Agent-Based Gaming for Policy 301
environment that is limited. They can interact with the simulated civilians. Here
the agent-based architecture allows for a realistic response of the simulated people,
such as obeying orders, discussing and changing of arousal, or engaging in aggressive
behavior. Essential is that the behavior of an artificial agent will affect the behavior of
other agents as well, which may lead to cascade effects. For example, if a policeman
decides to hit an agent, nearby agents will experience a higher arousal upon observing
a fellow agent being hit, and will probably have a higher tendency to either flee or
engage in aggressive behavior. In the game policemen can also communicate with
each other using a radio system (which can be blurred or fail in game scenarios).
At the command level, the player (which can be a group of people) has a generic
overview of the festival terrain, but they rely on the reports of the field-level players
for the interpretation of what is happening. The command level can give orders to
the field-level concerning their position and actions. Hence, they may decide to have
law enforcement as couples in the crowd, or cluster them in a line to block an area.
Also, they can give orders to the field-level players, such as arresting a civilian. Such
a single team game can be played to study optimal group compositions, learn about
how to manage turbulent situations in groups and as a practice tool. Also, such a
game can be played in a specific location on a confined time slot.
A multiple agent-based group game can target social complex systems where dif-
ferent groups are interacting with the agent population, but are having different and
sometimes conflicting interests. An example would be a team that is trying to stim-
ulate the diffusion of electric cars, which implies experts on technical possibilities,
economical viability, infrastructural development, and marketing and communi-
cation. Experts from these different domains have to develop and manage a joint
proposition toward segments in an artificial consumer base. At the same time, com-
parable teams can work on fuel-propelled cars, and thus compete with the electric
team for market share. Using a realistic artificial population that is based on empirical
data will provide a simulated environment where these different teams can operate.
Because in these settings is becomes impractical to meet in a specific location on
a confined time slot, it seems more realistic to implement multigroup agent-based
games as Internet games where different teams can operate quite independently, but
are being confronted with the implications of each other’s actions. In the multigroup
game teams also have to identify the strategy of competitors, and adapt to that, which
adds to the complexity of the game.
13.8 Experimentation with Management
The proposed game setting allows for measuring different task- and relationship-
oriented behaviors of the players. Critical factors to track are adaptive speed,
information collection, orientation on agent behavior, orientation on competitor be-
havior, and overall performance in terms of reaching predefined goals and agent
satisfaction. Experimentation will be possible to show the relation between the man-
agerial/leadership style of the player(s), and its success in turbulent settings. It will
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302 W. Jager and G. van der Vegt
also be possible as a post measurement to ask the players for their understanding of
the dynamics of the system using the methodology of Endsley (1995) and Edwards
et al. (2006), which focuses on measuring situation awareness in complex situations
and the identification of relations. This will contribute also to a more qualitative
indication of the extent to which they understand the complex nature of the game
and the best styles and strategies within social complex systems. Experiments thus
are capable of showing: (1) what leadership and managerial styles perform best in
conditions of varying complexity (single vs. multi player) and the type of popula-
tion (importance of social need), (2) how task- and relationship-oriented behavior,
adaptive speed, information collection, agent, and competitor orientation relate to
performance in management under different conditions of complexity, and (3) what
leadership and managerial styles are more effective in developing an understanding
of the system.
13.9 Conclusions and Discussion
In this chapter, we discussed how agent-based gaming may provide a methodology
to study effective management and leadership on social complex systems. Because
the major challenges in our society often deal with behavior change of large pop-
ulations and social complex processes, turbulent developments are common. This
proposed direction of development of methodology may contribute to a better under-
standing of how our species can manage common behavior, both among ourselves
and in interaction with our habitat. A critical contribution will be the formalization
of some main principles of human behavior in an integrated model, which subse-
quently allows for interaction with an artificial but realistically behaving population.
This will facilitate the transfer of behavioral insights into the realm of policy testing
and will contribute to the development of the required knowledge on how to manage
social complex behaving systems. Currently, the respective fields of social simula-
tion, management science, and gaming technology seem developed far enough to
make such an endeavor possible, but, considering agent-based gaming projects will
require the interaction between many different actors, also, here turbulences can be
expected.
Acknowledgment This chapter has been written in the context of the eGovPoliNet project. More
information can be found on http://www.policy-community.eu/
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Chapter 14
The Role of Microsimulation in the Development
of Public Policy
Roy Lay-Yee and Gerry Cotterell
Abstract This chapter seeks to provide a brief introduction to the method of mi-
crosimulation and its utility for the development of public policy. Since the inception
of microsimulation in the 1950s, its use for policy purposes has extended from the
economic to other domains as data availability and technological advances have
burgeoned. There has also been growing demand in recent times to address increas-
ingly complex policy issues that require new approaches. Microsimulation focuses
on modelling individual units and the micro-level processes that affect their devel-
opment, be they people’s lives or other trajectories. It comes in various types, for
example along the dimensions of arithmetical or behavioural, and static or dynamic.
It has its own distinctive model-building process, which relies on empirical data and
derived parameters with an insertion of chance to simulate realistic distributions.
The particular utility of microsimulation for policy development lies in its ability to
combine multiple sources of information in a single contextualised model to answer
‘what if’ questions on complex social phenomena and issues.
14.1 Introduction
This chapter provides an introduction to the method of microsimulation and its role
in the development of public policy. The chapter firstly provides a brief history of
microsimulation, which tracks the development of this method since its inception
in the 1950s. This is followed by an explanation of microsimulation itself, the dif-
ferences between its various types, and the model-building procedure. We end with
assessing the utility of microsimulation for policy development, and setting out its
strengths and weaknesses as a method for this purpose. We describe a case study
taken from a dynamic microsimulation model of the early life course developed in
New Zealand. In the Appendix, we provide selected examples of a number of other
existing microsimulation models in use from around the world.
R. Lay-Yee (�) · G. Cotterell
Centre of Methods and Policy Application in the Social Sciences (COMPASS Research Centre),
University of Auckland, Private Bag 92019, 1142 Auckland, New Zealand
e-mail: r.layyee@auckland.ac.nz
© Springer International Publishing Switzerland 2015 305
M. Janssen et al. (eds.), Policy Practice and Digital Science,
Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_14
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306 R. Lay-Yee and G. Cotterell
14.2 A Brief History
The academic literature generally acknowledges the pioneering work of Guy Orcutt
in his 1957 paper, ‘A new type of socio-economic system’, as providing the founda-
tion for the field of microsimulation. In his paper, Orcutt posited that microsimulation
models consisting of ‘various sorts of interacting units which receive inputs and gen-
erate outputs’ (Orcutt 1957, p. 117) could be used to investigate ‘what would happen
given specified external conditions and governmental actions’ (Orcutt 1957, p. 122).
Even today, Orcutt’s prescient ideas still provide a relevant blueprint for modelling.
However, the potential of this new approach was slow to materialise because of
limitations in computing power and the lack of suitable data.
As these respective limitations were gradually overcome with the rise of tech-
nology and data collection, the use of microsimulation increased. In the 1970s,
microsimulation models were being used in the USA to assist the development of
social policy (Citro and Hanusek 1991), and by 1990 ‘microsimulation had be-
come widespread enough in the domain of tax and transfer analysis’ (Anderson and
Hicks 2011, p. 1). Merz identified six major microsimulation projects in the USA
and Europe in the 1980s, growing to 18 in the 1970s and then to 33 in the 1980s
(Merz 1991). Further rapid advances in computing power, along with information