Complex systems in the social and behavioral sciences : theory, method and application / Euel Elliott and L. Douglas Kiel.

Format:

Book

Author/Creator:

Kiel, L. Douglas.

Contributor:

Elliott, Euel W.

Language:

English

Subjects (All):

Social sciences--Methodology.

Social sciences.

Psychology--Methodology.

Psychology.

System theory.

Complexity (Philosophy).

Physical Description:

1 online resource (389 pages)

Edition:

1st ed.

Place of Publication:

Ann Arbor, Michigan : University of Michigan Press, 2021.

Summary:

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 authors 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, and offer new insights into this fascinating area of research.

Contents:

Cover

Half Title

Title Page

Copyright Page

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: Adaptation, Coordination and Synchronization

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: 1999-2018

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: A Narrative Review

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

Modeling Patterns of Insurgent Conflict

Agent-Based Models of Intra-State Conflict.

Future Directions

Part 2 Complexity, Computation and Artificial Intelligence

Chapter 4 Novelty Production and Evolvability in Digital Genomic Agents: Logical Foundations and Policy Design Implications of Complex Adaptive Systems

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

Chapter 5 The Game of Go: Bounded Rationality and Artificial Intelligence

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

Appendix.

Part 3 Simulating Complexity: Agent-Based Modeling

Chapter 6 Agent-Based Modeling: Challenges and Prospects

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

Chapter 7 An Agent-Based Model of Obesity and Policy Implications

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

Part 4 Scaling and Self-Organization

Chapter 8 It's About Time

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

Part 5 Philosophy of Science and Epistemology

Chapter 10 Philosophy of Science, Network Theory and Conceptual Change: Paradigm Shifts as Information Cascades

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

Forms of Complexity?

Dynamic Complexity and Knowledge

Knowledge Problems of Computational Complexity

Complexity Foundations of Bounded Rationality and Limited Knowledge

Knowledge and Ergodicity

Chapter 12 Biological Hypercomputation: Social and Political Implications

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.

Notes:

Includes bibliographical references (pages 365-419) and index.

Description based on information from the publisher.

Description based on publisher supplied metadata and other sources.

ISBN:

9780472128921

0472128922

OCLC:

1231458871