This book introduces the concept of policy decision emergence and its dynamics at the sub systemic level of the decision process. This level constitutes the breeding ground of the emergence of policy decisions but remains unexplored due to the absence of adequate tools. It is a nonlinear complex system made of several entities that interact dynamically. The behavior of such a system cannot be understood with linear and deterministic methods.
The book presents an innovative multidisciplinary approach that results in the development of a Policy Decision Emergence Simulation Model (PODESIM). This computational model is a multi-level fuzzy inference system that allows the identification of the decision emergence levers.
This development represents a major advancement in the field of public policy decision studies. It paves the way for decision emergence modeling and simulation by bridging complex systems theory, multiple streams theory, and fuzzy logic theory.
Author(s): Ali Guidara
Series: Studies in Fuzziness and Soft Computing, 405
Publisher: Springer
Year: 2021
Language: English
Pages: 130
City: Cham
Preface
Synopsis
Contents
1 Introduction
References
2 Decision Process and Analytical Frameworks—Levels of Analysis and Paradigmatic Evolution
2.1 Decision Analysis Frameworks
2.1.1 Rational Decision-Making Model
2.1.2 Incremental Model
2.1.3 Bureaucratic Politics Model
2.1.4 Garbage Can Model
2.1.5 Chapter’s Conclusion
2.2 Decision Emergence as a Complex System
References
3 Complex Systems and Public Policy
3.1 Properties of Complex Systems
3.2 Modeling and Simulation of Complex Systems
3.2.1 Modeling
3.2.2 Simulation
3.3 Stacey Matrix: A Complexity Tool
References
4 Multiple Streams Theory
4.1 Basic Foundations of Multiple Streams Theory
4.1.1 Problem Stream
4.1.2 Policy Stream
4.1.3 Politics Stream
4.2 Assessment of Multiple-Streams Theory
4.3 Paradigmatic and Methodological Transition
References
5 Artificial Intelligence and Fuzzy Logic
5.1 Artificial and Computational Intelligence
5.2 Fuzzy Logic
5.3 Fuzzy Sets
5.4 Fuzzy Logic as a Decision Tool
5.5 Fuzzy Inference Systems: Principles and Modelling
5.5.1 Structure and Modelling of Fuzzy Inference Systems
5.5.2 Fuzzy Inference Algorithms
5.6 Mamdani Fuzzy Model
5.6.1 Fuzzification of Input Variables
5.6.2 Membership Functions
5.6.3 Fuzzy Inference Rules and Logical Operators
5.6.4 Defuzzification
5.6.5 Modeling and Simulation Tools
5.7 Conclusion
References
6 PODESIM—Policy Decision Emergence Simulation Model
6.1 Membership Functions of PODESIM
6.2 Fuzzy Inference Rules of PODESIM
6.3 Case Study—Model Validation and Results
6.3.1 Empirical Case Choice
6.3.2 Data Collection
6.3.3 Simulation and Results
6.3.4 Methodological Interpretation of Simulation Results
References
7 Analysis of Results
7.1 Day-by-Day Analysis
7.2 Conclusion
7.3 PODESIM Limitations and Further Development
References
8 Innovation and Contributions
References
9 Conclusion and Future Research
References
Uncited References
