• Modeling Social Behavior
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Modeling Social Behavior

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A comprehensive introduction to mathematical and agent-based modeling of social behaviorThis book provides a unified, theory-driven introduction to key mathematical and agent-based models of social...
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"An important contribution to the field of social dynamics."---Adriana A. Mal... Read More
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  • Format:
  • Publication Date: 03 October 2023
  • ISBN: 9780691224145
  • Pages: 360
  • Imprint: Princeton University Press
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A comprehensive introduction to mathematical and agent-based modeling of social behavior

This book provides a unified, theory-driven introduction to key mathematical and agent-based models of social dynamics and cultural evolution, teaching readers how to build their own models, analyze them, and integrate them with empirical research programs. It covers a variety of modeling topics, each exemplified by one or more archetypal models, and helps readers to develop strong theoretical foundations for understanding social behavior. Modeling Social Behavior equips social, behavioral, and cognitive scientists with an essential tool kit for thinking about and studying complex social systems using mathematical and computational models.

  • Combines both mathematical and agent-based modeling of social behavior
  • Integrates cognitive science, social science, and cultural evolution
  • Covers topics such as the philosophy of modeling, collective movement, segregation, contagion, polarization, the evolution of cooperation, the emergence of norms, networks, and the scientific process
  • Discusses more advanced topics, including how to use models to build a more robust empirical research program
  • An ideal introductory textbook for graduate students or advanced undergraduates
  • An invaluable resource for practitioners
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"An important contribution to the field of social dynamics."---Adriana A. Maldonado-Chaparro, The Quarterly Review of Biology
Paul E. Smaldino is professor of cognitive and information sciences at the University of California, Merced, and external professor at the Santa Fe Institute.
  • Preface
    • How to Use This Book
    • Why This Book Uses NetLogo But You Don’t Have To
    • Summary of Chapters
    • Acknowledgments
  • 1 Doing Violence to Reality
    • 1.1 Flocking Birds and Boids
    • 1.2 What Are Models?
    • 1.3 The Parable of the Cubist Chicken
    • 1.4 Decomposition
    • 1.5 Formal Theory in the Inexact Sciences
    • 1.6 Why Model?
    • 1.7 Some Models of Note
    • 1.8 Equation-Based Models and Agent-Based Models
    • 1.9 Fine-Grained and Coarse-Grained Models
    • 1.10 The Journey Begins
  • 2 Particles
    • 2.1 NetLogo Basics
    • 2.2 Programming Basics
    • 2.3 Particle World
    • 2.4 Coding the Model
    • 2.5 The Components of a Model
    • 2.6 Describing a Model
    • 2.7 Flocking
    • 2.8 Reflections
    • 2.9 Going Deeper
    • 2.10 Exploration
  • 3 The Schelling Chapter
    • 3.1 The Puzzle of Segregation
    • 3.2 A Model of Segregation
    • 3.3 A Formal Description of the Model
    • 3.4 Thinking about Consequences
    • 3.5 Coding the Model
    • 3.6 The Power of Play
    • 3.7 Model Analysis
    • 3.8 Analyzing the Segregation Model
    • 3.9 Reflections
    • 3.10 Going Deeper
    • 3.11 Exploration
  • 4 Contagion
    • 4.1 The Diffusion of Innovations
    • 4.2 Spontaneous Adoption
    • 4.3 Social Influence: The SI Model
    • 4.4 The Analytical SI Model
    • 4.5 Getting Better: The SIS Model
    • 4.6 Staying Better: The SIR Model
    • 4.7 Reflections
    • 4.8 Going Deeper
    • 4.9 Exploration
  • 5 Opinion Dynamics
    • 5.1 Building a Model of Opinion Dynamics
    • 5.2 Opinion Dynamics Under Positive Influence
    • 5.3 Bounded Confidence
    • 5.4 Negative Influence
    • 5.5 Multiple Opinions, Polarization, and Extremism
    • 5.6 Reflections
    • 5.7 Going Deeper
    • 5.8 Exploration
  • 6 Cooperation
    • 6.1 The Prisoner’s Dilemma
    • 6.2 Evolutionary Dynamics
    • 6.3 Cooperation and Assortment
    • 6.4 Reducing Assortment
    • 6.5 Positive Assortment and Hamilton’s Rule
    • 6.6 Reciprocity
    • 6.7 The Evolutionary Stability of Reciprocity
    • 6.8 Reflections
    • 6.9 Going Deeper
    • 6.10 Exploration
  • 7 Coordination
    • 7.1 Norms with Symmetric Payoffs
    • 7.2 Group-Beneficial Norms
    • 7.3 Group-Beneficial Norms in a Structured Population
    • 7.4 Division of Labor
    • 7.5 Reflections
    • 7.6 Going Deeper
    • 7.7 Exploration
  • 8 The Scientific Process
    • 8.1 Science as Hypothesis Testing
    • 8.2 Bayes’ Theorem
    • 8.3 Science as Bayesian Inference
    • 8.4 Science as a Population Process
    • 8.5 Science as a Cultural Process
    • 8.6 Why the Most Newsworthy Science Might Be the Least Trustworthy
    • 8.7 Reflections
    • 8.8 Going Deeper
    • 8.9 Exploration
  • 9 Networks
    • 9.1 Network Building Blocks
    • 9.2 Network Architectures: Order and Chaos
    • 9.3 Small-World Networks: Short Paths and Strong Clustering
    • 9.4 Simple vs. Complex Contagion on Small-World Networks
    • 9.5 Preferential Attachment
    • 9.6 Other Social Drivers of Network Structure
    • 9.7 Reflections
    • 9.8 Going Deeper
    • 9.9 Exploration
  • 10 Models and Reality
    • 10.1 The Mapping Problem
    • 10.2 Nine Lessons for Turning an Idea into a Model
    • 10.3 Analyzing Your Model in Light of Itself
    • 10.4 Analyzing Your Model in Light of Empirical Data
    • 10.5 Fitting Models to Data on a Rugged Landscape
    • 10.6 Reflections
  • 11 Maps and Territories
    • 11.1 The Map Is Not The Territory
    • 11.2 We Need Many Maps
    • 11.3 The Journey Continues
  • Image Credits
  • Bibliography
  • Index