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Complex Systems in the Social and Behavioral Sciences: Theory, Method and Application
Euel Elliott and L. Douglas Kiel, eds.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.
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Cover
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Half Title
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Title Page
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Copyright Page
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Contents
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List of Figures
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List of Tables
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Introduction
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What are Complex Adaptive Systems?
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Agent-Based Modeling: Simulating Complexity
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Gödel, Turing and Complexity
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Beyond the Classical Turing Machine
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Overview of Chapters
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Part 1: Social Systems Levels and Complexity
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Part 2: Complexity, Computation and Artificial Intelligence
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Part 3: Simulating Complexity: Agent-Based Modeling
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Part 4: Scaling and Self-Organization
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Part 5: Philosophy of Science and Epistemology
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Concluding Comments
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Part 1 Social Systems Levels and Complexity
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Chapter 1 Group Dynamics: Adaptation, Coordination and Synchronization
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Introduction
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Adaptive Behavior
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Background
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Complex Adaptive Systems
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Coordination
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Shared Mental Models
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Nonlinear Dynamics of Coordination
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Intersection Experiments
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Stag Hunt Games
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Social Loafing
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Self-Efficacy
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Evolutionary Stag Hunt Games
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Dynamics and Group Size
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Synchronization
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Principles
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Measurement of Synchronization
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Team Performance
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Summary
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Chapter 2 Complexity Science and the Organization Sciences: 1999–2018
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Introduction: The Roots of Complexity Science in the Organizational Sciences
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Evolution and Revolution in Scientific Paradigms
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Methods
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Complexity Science Publications in Financial Times Organization Science Journals
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Discussion
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Conclusion
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Chapter 3 Complexity Science and the Study of Armed Conflict: A Narrative Review
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Introduction
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Complexity and Conflict
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Past: Prior to 2000
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Theory
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Data
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Methods
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Mathematical Models of Arms Races
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Mathematical Models of Armed Conflict
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Computational Models of Armed Conflict
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Present: 2000–2019
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Theory
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Data
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Methods
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Modeling Patterns of Insurgent Conflict
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Agent-Based Models of Intra-State Conflict
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Future Directions
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Theory
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Data
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Methods
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Conclusion
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Part 2 Complexity, Computation and Artificial Intelligence
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Chapter 4 Novelty Production and Evolvability in Digital Genomic Agents: Logical Foundations and Policy Design Implications of Complex Adaptive Systems
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Introduction
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G-T-P Logic Condition (ii) and Evidence from Genomic Evolution of Self-Ref and Self-Rep
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Online Self-Assembly with Self-Ref, Machine Execution and Offline Self-Rep in Immuno-Cognitive Systems
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Self-Ref Machinery
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Self-Rep Mirror System
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GTP Logic Condition (iii): The Liar Strategy/Malware, Contrarian Structures, and Who do You Need to Surprise?
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Self-Halting Machines and Theorems of the Systems
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Malware/Liar Strategy Function and V-D-J-Based T-Cell Detection of Non-Self Pathogens
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The Liar/Malware Strategy fp¬
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Bio-Informatics of T-Cell Training
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The Gödel Sentence
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Extant Strategic and Regulatory Frameworks Relating to Contrarian Oppositional Structures and Innovative Rule Breaking
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Self-Reflexive Stock Market Games, Arthur (1994) and Contrarian/Minority Payoff Structures in Arthur et al. (1997)
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Lucas’s (1972) Thesis on Surprise Policy Strategy and Widespread Policy Failure
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Pre-Commitment to Fixed Rule to Vitiate Surprise Inflation: The Serial Collapse of Currency Pegs and the Soros Liar Strategy
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Kant, Hayek and Hirschman: Rules, Principles and Discretion
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Conclusion
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Chapter 5 The Game of Go: Bounded Rationality and Artificial Intelligence
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Introduction
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The Intertwined Domains of Bounded Rationality and Artificial Intelligence
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Bounded Rationality
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Artificial Intelligence
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AI: Toward Bounded Rationality or Rationality?
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Bounded Rationality and the Evolution of Go-Playing Computers
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Knowledge
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Search
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Learning
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The Complexity of Go: A Cellular Automata Perspective
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Conclusion
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Appendix
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Part 3 Simulating Complexity: Agent-Based Modeling
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Chapter 6 Agent-Based Modeling: Challenges and Prospects
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Academic Communities
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The Evolution of Agents
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The Challenge of Learning
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Critiques of Agent-Based Modeling
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Verification and Validation Challenges
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Public Policy and Agent-Based Modeling
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Emerging Prospects for Agent-Based Modeling
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Conclusion
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Chapter 7 An Agent-Based Model of Obesity and Policy Implications
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Introduction
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Background
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Verification
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Validation
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Model Description
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Verification of the Model of Obesity
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Trace Experiment with Statistical Debugging
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Sensitivity Analysis Experiment
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Validation of the Model of Obesity
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Calibration Experiment
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Cross-Model Validation
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Future Policies in Obesity Prevention and Reduction
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Discussion
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Conclusion
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Part 4 Scaling and Self-Organization
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Chapter 8 It’s About Time
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Introduction
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Time and Allometry
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Allometry/Information Hypothesis
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Outline
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Fractional Probability Calculus
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Temporal Allometry Relations
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Information Stability
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Conclusions
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Appendix
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A. Subordination of Time
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B. Solution to FKE
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Chapter 9 Lessons from Collective Intelligence
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The Social Insect Colony
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Adaptive Complex Systems
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Decision-Making: Rational, Non-Rational and Irrational
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Decision-Making by Social Insect Colonies
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The Utility of Complex Systems Methods
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The Use of Complex Systems Methods: Nest Emigrations
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The Use of Complex Systems Methods: Universality
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Modeling Collective Intelligence
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Conclusions
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Part 5 Philosophy of Science and Epistemology
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Chapter 10 Philosophy of Science, Network Theory and Conceptual Change: Paradigm Shifts as Information Cascades
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Introduction
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Scientific Paradigms: A First Model
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Modeling Popperian Falsification
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Modeling Kuhnian Dynamics
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Modeling the History of Scientific Change: Popper and Kuhn
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Directed Networks: A Second Model of Scientific Paradigms
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Science and Self-Organized Criticality
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Conclusion: Prospects for Expanding the Models
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Chapter 11 Complexity and Knowledge
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Introduction
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Forms of Complexity?
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Dynamic Complexity and Knowledge
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Knowledge Problems of Computational Complexity
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Complexity Foundations of Bounded Rationality and Limited Knowledge
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Knowledge and Ergodicity
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Conclusions
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Chapter 12 Biological Hypercomputation: Social and Political Implications
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Introduction
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Complexity Science is Ultimately About Gaining Degrees of Freedom
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Biological Hypercomputation: A First-Hand Approach
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Biological Hypercomputation: A Computational Understanding
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Social and Political Implications
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Drawing Conclusions
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References
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Contributors
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Index
- 978-0-472-12892-1 (ebook)
- 978-0-472-07488-4 (hardcover)
- 978-0-472-05488-6 (paper)