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

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

Introduction

Background

Complex Adaptive Systems

Coordination

Nonlinear Dynamics of Coordination

Intersection Experiments

Stag Hunt Games

Social Loafing

Evolutionary Stag Hunt Games

Dynamics and Group Size

Principles

Measurement of Synchronization

Team Performance

Summary

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

Introduction

Complexity and Conflict

Past: Prior to 2000

Theory

Data

Mathematical Models of Arms Races

Mathematical Models of Armed Conflict

Computational Models of Armed Conflict

Present: 2000–2019

Theory

Data

Modeling Patterns of Insurgent Conflict

Agent-Based Models of Intra-State Conflict

Theory

Data

Methods

Conclusion

Part 2 Complexity, Computation and Artificial Intelligence

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

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

Introduction

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

Introduction

Background

Verification

Validation

Model Description

Trace Experiment with Statistical Debugging

Sensitivity Analysis Experiment

Calibration Experiment

Cross-Model Validation

Future Policies in Obesity Prevention and Reduction

Discussion

Conclusion

Part 4 Scaling and Self-Organization

Introduction

Time and Allometry

Allometry/Information Hypothesis

Fractional Probability Calculus

Temporal Allometry Relations

Information Stability

Conclusions

A. Subordination of Time

B. Solution to FKE

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

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

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

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