Complex Systems in the Social and Behavioral Sciences: Theory, Method and Application

✍ Scribed by L. Douglas Kiel, Euel W. Elliott

• Publisher: University of Michigan Press
• Year: 2021
• Tongue: English
• Leaves: 452
• Category: Library

✦ Synopsis

Complexity Systems in the Social and Behavioral Sciences provides a sophisticated yet accessible account of complexity science or complex systems research. Phenomena in the behavioral, social, and hard sciences all exhibit certain important similarities consistent with complex systems. These include the concept of emergence, sensitivity to initial conditions, and interactions between agents in a system that yield unanticipated, nonlinear outcomes. The topics discussed range from the implications for artificial intelligence and computing to questions about how to model complex systems through agent-based modeling, to complex phenomena exhibited in international relations, and in organizational behavior. This volume will be an invaluable addition for both the general reader and the specialist, offering new insights into this fascinating area of research.

✦ Table of Contents

Cover
Half-title
Title page
Copyright information
Table of contents
List of figures
List of tables
Introduction
What are Complex Adaptive Systems?
Agent-Based Modeling: Simulating Complexity
Gödel, Turing and Complexity
Beyond the Classical Turing Machine
Overview of Chapters
Part 1: Social Systems Levels and Complexity
Part 2: Complexity, Computation and Artificial Intelligence
Part 3: Simulating Complexity: Agent- Based Modeling
Part 4: Scaling and Self- Organization
Part 5: Philosophy of Science and Epistemology
Concluding Comments
Part 1 Social Systems Levels and Complexity
Chapter 1 Group Dynamics
Introduction
Adaptive Behavior
Background
Complex Adaptive Systems
Coordination
Shared Mental Models
Nonlinear Dynamics of Coordination
Intersection Experiments
Stag Hunt Games
Social Loafing
Self-Efficacy
Evolutionary Stag Hunt Games
Dynamics and Group Size
Synchronization
Principles
Measurement of Synchronization
Team Performance
Summary
Chapter 2 Complexity Science and the Organization Sciences
Introduction: The Roots of Complexity Science in the Organizational Sciences
Evolution and Revolution in Scientific Paradigms
Methods
Complexity Science Publications in Financial Times Organization Science Journals
Discussion
Conclusion
Chapter 3 Complexity Science and the Study of Armed Conflict
Introduction
Complexity and Conflict
Past: Prior to 2000
Theory
Data
Methods
Mathematical Models of Arms Races
Mathematical Models of Armed Conflict
Computational Models of Armed Conflict
Present: 2000–2019
Theory
Data
Methods
Modeling Patterns of Insurgent Conflict
Agent-Based Models of Intra-State Conflict
Future Directions
Theory
Data
Methods
Conclusion
Part 2 Complexity, Computation and Artificial Intelligence
Chapter 4 Novelty Production and Evolvability in Digital Genomic Agents
Introduction
G-T-P Logic Condition (ii) and Evidence from Genomic Evolution of Self-Ref and Self-Rep
Online Self-Assembly with Self-Ref, Machine Execution and Offline Self-Rep in Immuno-Cognitive Systems
Self-Ref Machinery
Self-Rep Mirror System
GTP Logic Condition (iii): The Liar Strategy/Malware, Contrarian Structures, and Who do You Need to Surprise?
Self-Halting Machines and Theorems of the Systems
Malware/Liar Strategy Function and V-D-J-Based T-Cell Detection of Non-Self Pathogens
The Liar/Malware Strategy fp¬
Bio-Informatics of T-Cell Training
The Gödel Sentence
Extant Strategic and Regulatory Frameworks Relating to Contrarian Oppositional Structures and Innovative Rule Breaking
Self-Reflexive Stock Market Games, Arthur (1994) and Contrarian/Minority Payoff Structures in Arthur et al. (1997)
Lucas’s (1972) Thesis on Surprise Policy Strategy and Widespread Policy Failure
Pre-Commitment to Fixed Rule to Vitiate Surprise Inflation: The Serial Collapse of Currency Pegs and the Soros Liar Strategy
Kant, Hayek and Hirschman: Rules, Principles and Discretion
Conclusion
Chapter 5 The Game of Go
Introduction
The Intertwined Domains of Bounded Rationality and Artificial Intelligence
Bounded Rationality
Artificial Intelligence
AI: Toward Bounded Rationality or Rationality?
Bounded Rationality and the Evolution of Go-Playing Computers
Knowledge
Search
Learning
The Complexity of Go: A Cellular Automata Perspective
Conclusion
Appendix
Part 3 Simulating Complexity: Agent-Based Modeling
Chapter 6 Agent-Based Modeling
Academic Communities
The Evolution of Agents
The Challenge of Learning
Critiques of Agent-Based Modeling
Verification and Validation Challenges
Public Policy and Agent-Based Modeling
Emerging Prospects for Agent-Based Modeling
Conclusion
Chapter 7 An Agent-Based Model of Obesity and Policy Implications
Introduction
Background
Verification
Validation
Model Description
Verification of the Model of Obesity
Trace Experiment with Statistical Debugging
Sensitivity Analysis Experiment
Validation of the Model of Obesity
Calibration Experiment
Cross-Model Validation
Future Policies in Obesity Prevention and Reduction
Discussion
Conclusion
Part 4 Scaling and Self-Organization
Chapter 8 It’s About Time
Introduction
Time and Allometry
Allometry/Information Hypothesis
Outline
Fractional Probability Calculus
Temporal Allometry Relations
Information Stability
Conclusions
Appendix
A. Subordination of Time
B. Solution to FKE
Chapter 9 Lessons from Collective Intelligence
The Social Insect Colony
Adaptive Complex Systems
Decision-Making: Rational, Non-Rational and Irrational
Decision-Making by Social Insect Colonies
The Utility of Complex Systems Methods
The Use of Complex Systems Methods: Nest Emigrations
The Use of Complex Systems Methods: Universality
Modeling Collective Intelligence
Conclusions
Part 5 Philosophy of Science and Epistemology
Chapter 10 Philosophy of Science, Network Theory and Conceptual Change
Introduction
Scientific Paradigms: A First Model
Modeling Popperian Falsification
Modeling Kuhnian Dynamics
Modeling the History of Scientific Change: Popper and Kuhn
Directed Networks: A Second Model of Scientific Paradigms
Science and Self-Organized Criticality
Conclusion: Prospects for Expanding the Models
Chapter 11 Complexity and Knowledge
Introduction
Forms of Complexity?
Dynamic Complexity and Knowledge
Knowledge Problems of Computational Complexity
Complexity Foundations of Bounded Rationality and Limited Knowledge
Knowledge and Ergodicity
Conclusions
Chapter 12 Biological Hypercomputation
Introduction
Complexity Science is Ultimately About Gaining Degrees of Freedom
Biological Hypercomputation: A First-Hand Approach
Biological Hypercomputation: A Computational Understanding
Social and Political Implications
Drawing Conclusions
References
Contributors
Index