When looking at complexity in policy-making, chapter 4 has provided an illustration to offer insights to future business leaders to understand that, despite the inherent unpredictability of environments, organisms survive and develop intricate webs of interdependence in terms of their ecologies due to the adaptive capacities of organisms that allow them to self-organize. 

Chapter 4: Q1: According to the author’s assessment, what’s the importance of this scenario to the policy-making process? Briefly explain.

Identify the importance of that scenario

provide a short and clear narrative to support your response above

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Chapter 5, Figure 5.1 is an illustration of the future state of practice of systems modeling and simulation. With this mind, the adoption of recent, current and expected innovations could result in the future of the art of systems modeling. From this illustration, we have learned that it would be possible to simultaneously use multiple hypotheses to achieve different goals that could include the search for deeper understanding and policy insights, experimentations in virtual laboratories, future oriented-explorations, robust policy design, and robustness testing under deep uncertainty. 

Q1: From the assessment by the authors, what are the main advantages of using multiple models? Briefly explain.

Identify and name the advantages of using multiple models

provide a short and clear narrative to support your responses

Chapters 4,5 and 6

Into policymaking and modeling in a complex world

From Building a model to adaptive robust decision- making using systems modelling

Features and added value of simulation Models using different modelling approaches supporting policymaking: A comparative analysis.

Chapter Goals and Objectives Overall – students will learn and understand
consequences of complexity in the real-world, and meaningful ways to understand and manage such situations
the implications of complexity and that many social systems are unpredictable by nature, especially when in the presence of structural change (transitions)
natural tendency to criticize the approaches that ignore difficulties and pretend to predict using simplistic models
that 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.

4. Policymaking and modeling in a complex world
the word “complexity” can be used to indicate a variety of kinds of difficulties
identification of complexity and uncertainty in policy-making
in very simple physical systems, interactions may give rise to complex behavior, expressed in different types of behavior, ranging from very stable to chaotic
reasons why complex adaptive systems have a strong capacity to self-organize
two of the ways systems are oversimplified: quantification and compartmentalization
models are assessed by their ability to predict/mirror observed aspects of the environments

5. From building a model to adaptive robust decision-making using systems modeling
System Dynamics Modeling and Simulation of Old
✓ methods for modeling and simulating dynamically complex systems
✓ evolutions in modeling and simulation with recent explosive growth in computational power, data, social media, to support decision-making
Recent Innovations and Expected Evolutions
✓ Why often seemingly more revolutionary—innovations have been introduced and demonstrated, but that they have not been massively adopted yet
Current and Expected Evolutions
✓ Three current evolutions expected to further reinforce – “experiential art” to “computational science.”
Future State of Practice of Systems Modeling and Simulation
✓ modeling and simulation with sparse data to modeling and simulation with (near real-time) big data;
✓ simulating and analyzing a few simulation runs to simulating and simultaneously analyzing well-selected ensembles of runs;
✓ using models for intuitive policy testing to using models as instruments for designing adaptive robust robust policies;
✓ developing educational flight simulators to fully integrated decision support.

Features and added value of simulation models using different modelling approaches to policy-making: A Comparative analysis
Foundations of Simulation Modelling
✓ model simplification definitions—smaller, less detailed, less complex,
Analysis of Simulation Models of Different Modelling Approaches
✓ main goal – describe and compare different simulation models to identify similarities,
✓ Identification of the differences that suggest useful approaches, tools and techniques
✓ present a comparative analysis of different simulation models with respect to their role in public decision-making processes
✓ investigate the differences between simulation models and underlying modelling theories to find variables that impact effectiveness of simulation models in policy-making ✓ main benefits – provide an understanding of the peculiarities and the added value of different kinds of simulation models generated on the basis of particular modelling approaches
✓ Demonstrate how existing approaches to policy simulation can be combined to effectively support public policy-making in a comprehensive way

6. Features and added value of simulation Cont.…
MicroSim—Micro-simulation Model: Modelling the Swedish Population
✓ event-driven micro-simulation model with discrete time steps,
✓ developed for exploring the impact of different intervention policies based on vaccination, isolation and social distancing
Ocopomo’s Kosice Case – capture the behavior of key stakeholders, decisionmakers towards a new energy policy, and better housing alternatives using renewable energy sources
SKIN—Simulating Knowledge Dynamics in Innovation Networks
✓ SKIN is an agent-based model used to understand innovation policy initiatives
✓ contains heterogeneous agents that act and interact in a large-scale complex and changing social environment
✓ involved agents represent innovative actors that try to sell their innovations to other agents to produce their outputs
• Simulation Models and Discussion: Added Value Limitations of Models
– due to social complexity and non-linearity, it is difficult to describe processes and systems analytically (Gilbert and Troitzsch, 2005) – micro-simulation models are based on a weighted sum of a representative sample of the population
– consider characteristics of individuals and are able to reproduce social reality (Martini and Trivellato, 1997).
– can predict short-term and long-term impact of policies (Gilbert and Troitzsch 2005).
– micro-simulation models are costly to build and complex, especially at the level of data analysis requirements

References:
Janssen, M., et al. (eds.), Policy Practice and Digital Science, Public Administration and Information Technology 10, DOI 10.1007/978-3-319-12784-2_1