As most of us are IS/IT professionals and the concept of public policy is new for us, what do you think? What are some of your views about what you have read in the first chapter?

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

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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

citizens

Policy formulation

Policy
implementation

Policy
execution

Policy
enforcement and

evaluation

politicians

Policy-
makers

Administrative
organizations

businesses

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.

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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

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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.

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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

• Chapter 1 Introduction to Policy-Making in the Digital Age

1.1 Introduction
1.2 Complexity and Uncertainty in Policy-Making
1.3 Developments
1.3.1 The Availability of Big and Open Linked Data (BOLD)
1.3.2 Rise of Hybrid Simulation Approaches
1.3.3 Ubiquitous User Engagement
1.4 Combining Disciplines in E-government Policy-Making
1.5 Overview of Chapters
1.6 Conclusions