Complex Adaptive Systems: An Introduction to Computational Models of Social Life

Complex Adaptive Systems: An Introduction to Computational Models of Social Life

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This book provides the first clear, comprehensive, and accessible account of complex adaptive social systems, by two of the field's leading authorities. Such systems--whether political parties, stock markets, or ant colonies--present some of the most intriguing theoretical and practical challenges confronting the social sciences. Engagingly written, and balancing technical detail with intuitive explanations, Complex Adaptive Systems focuses on the key tools and ideas that have emerged in the field since the mid-1990s, as well as the techniques needed to investigate such systems. It provides a detailed introduction to concepts such as emergence, self-organized criticality, automata, networks, diversity, adaptation, and feedback. It also demonstrates how complex adaptive systems can be explored using methods ranging from mathematics to computational models of adaptive agents. John Miller and Scott Page show how to combine ideas from economics, political science, biology, physics, and computer science to illuminate topics in organization, adaptation, decentralization, and robustness. They also demonstrate how the usual extremes used in modeling can be fruitfully more

Product details

  • Paperback | 288 pages
  • 154.9 x 233.7 x 20.3mm | 430.92g
  • Princeton University Press
  • New Jersey, United States
  • English
  • 18 halftones. 16 line illus. 42 tables.
  • 0691127026
  • 9780691127026
  • 118,198

Back cover copy

"The use of computational, especially agent-based, models has already shown its value in illuminating the study of economic and other social processes. Miller and Page have written an orientation to this field that is a model of motivation and insight, making clear the underlying thinking and illustrating it by varied and thoughtful examples. It conveys with remarkable clarity the essentials of the complex systems approach to the embarking researcher."--Kenneth J. Arrow, winner of the Nobel Prize in economics "In Complex Adaptive Systems, two masters of this burgeoning field provide a highly readable and novel restatement of the logic of social interactions, linking individually based micro processes to macrosocial outcomes, ranging from Adam Smith's invisible hand to Thomas Schelling's models of standing ovations. The book combines the vision of a new Santa Fe school of computational, social, and behavioral science with essential 'how to' advice for apprentice modelers."--Samuel Bowles, author of Microeconomics: Behavior, Institutions, Evolution "This is a wonderful book that will be read by graduate students, faculty, and policymakers. The authors write in an extraordinarily clear manner about topics that are very technical and difficult for many people. I sat down to begin thumbing through and found myself deeply engaged."--Elinor Ostrom, author of Understanding Institutional Diversityshow more

About John H. Miller

John H. Miller is professor of economics and social sciences at Carnegie Mellon University. Scott E. Page is professor of complex systems, political science, and economics at the University of Michigan. He is the author of The Difference (Princeton).show more

Table of contents

List of Figures xiiiList of Tables xvPreface xviiPart I: Introduction 1Chapter 1: Introduction 3Chapter 2: Complexity in Social Worlds 92.1 The Standing Ovation Problem 102.2 What's the Buzz? 142.2.1 Stay Cool 142.2.2 Attack of the Killer Bees 152.2.3 Averaging Out Average Behavior 162.3 A Tale of Two Cities 172.3.1 Adding Complexity 202.4 New Directions 262.5 Complex Social Worlds Redux 272.5.1 Questioning Complexity 27Part II: Preliminaries 33Chapter 3: Modeling 353.1 Models as Maps 363.2 A More Formal Approach to Modeling 383.3 Modeling Complex Systems 403.4 Modeling Modeling 42Chapter 4: On Emergence 444.1 A Theory of Emergence 464.2 Beyond Disorganized Complexity 484.2.1 Feedback and Organized Complexity 50Part III: Computational Modeling 55Chapter 5: Computation as Theory 575.1 Theory versus Tools 595.1.1 Physics Envy: A Pseudo-Freudian Analysis 625.2 Computation and Theory 645.2.1 Computation in Theory 645.2.2 Computation as Theory 675.3 Objections to Computation as Theory 685.3.1 Computations Build in Their Results 695.3.2 Computations Lack Discipline 705.3.3 Computational Models Are Only Approximations to Specific Circumstances 715.3.4 Computational Models Are Brittle 725.3.5 Computational Models Are Hard to Test 735.3.6 Computational Models Are Hard to Understand 765.4 New Directions 76Chapter 6: Why Agent-Based Objects? 786.1 Flexibility versus Precision 786.2 Process Oriented 806.3 Adaptive Agents 816.4 Inherently Dynamic 836.5 Heterogeneous Agents and Asymmetry 846.6 Scalability 856.7 Repeatable and Recoverable 866.8 Constructive 866.9 Low Cost 876.10 Economic E. coli (E. coni?) 88Part IV: Models of Complex Adaptive Social Systems 91Chapter 7: A Basic Framework 937.1 The Eightfold Way 937.1.1 Right View 947.1.2 Right Intention 957.1.3 Right Speech 967.1.4 Right Action 967.1.5 Right Livelihood 977.1.6 Right Effort 987.1.7 Right Mindfulness 1007.1.8 Right Concentration 1017.2 Smoke and Mirrors: The Forest Fire Model 1027.2.1 A Simple Model of Forest Fires 1027.2.2 Fixed, Homogeneous Rules 1027.2.3 Homogeneous Adaptation 1047.2.4 Heterogeneous Adaptation 1057.2.5 Adding More Intelligence: Internal Models 1077.2.6 Omniscient Closure 1087.2.7 Banks 1097.3 Eight Folding into One 1107.4 Conclusion 113Chapter 8: Complex Adaptive Social Systems in One Dimension 1148.1 Cellular Automata 1158.2 Social Cellular Automata 1198.2.1 Socially Acceptable Rules 1208.3 Majority Rules 1248.3.1 The Zen of Mistakes in Majority Rule 1288.4 The Edge of Chaos 1298.4.1 Is There an Edge? 1308.4.2 Computation at the Edge of Chaos 1378.4.3 The Edge of Robustness 139Chapter 9: Social Dynamics 1419.1 A Roving Agent 1419.2 Segregation 1439.3 The Beach Problem 1469.4 City Formation 1519.5 Networks 1549.5.1 Majority Rule and Network Structures 1589.5.2 Schelling's Segregation Model and Network Structures 1639.6 Self-Organized Criticality and Power Laws 1659.6.1 The Sand Pile Model 1679.6.2 A Minimalist Sand Pile 1699.6.3 Fat-Tailed Avalanches 1719.6.4 Purposive Agents 1759.6.5 The Forest Fire Model Redux 1769.6.6 Criticality in Social Systems 177Chapter 10: Evolving Automata 17810.1 Agent Behavior 17810.2 Adaptation 18010.3 A Taxonomy of 2 x 2 Games 18510.3.1 Methodology 18710.3.2 Results 18910.4 Games Theory: One Agent, Many Games 19110.5 Evolving Communication 19210.5.1 Results 19410.5.2 Furthering Communication 19710.6 The Full Monty 198Chapter 11: Some Fundamentals of Organizational Decision Making 20011.1 Organizations and Boolean Functions 20111.2 Some Results 20311.3 Do Organizations Just Find Solvable Problems? 20611.3.1 Imperfection 20711.4 Future Directions 210Part V: Conclusions 211Chapter 12: Social Science in Between 21312.1 Some Contributions 21412.2 The Interest in Between 21812.2.1 In between Simple and Strategic Behavior 21912.2.2 In between Pairs and Infinities of Agents 22112.2.3 In between Equilibrium and Chaos 22212.2.4 In between Richness and Rigor 22312.2.5 In between Anarchy and Control 22512.3 Here Be Dragons 225Epilogue 227The Interest in Between 227Social Complexity 228The Faraway Nearby 230AppendixesA An Open Agenda For Complex Adaptive Social Systems 231A.1 Whither Complexity 231A.2 What Does it Take for a System to Exhibit ComplexBehavior? 233A.3 Is There an Objective Basis for Recognizing Emergence andComplexity? 233A.4 Is There a Mathematics of Complex Adaptive Social Systems? 234A.5 What Mechanisms Exist for Tuning the Performance ofComplex Systems? 235A.6 Do Productive Complex Systems Have Unusual Properties? 235A.7 Do Social Systems Become More Complex over Time 236A.8 What Makes a System Robust? 236A.9 Causality in Complex Systems? 237A.10 When Does Coevolution Work? 237A.11 When Does Updating Matter? 238A.12 When Does Heterogeneity Matter? 238A.13 How Sophisticated Must Agents Be Before They Are Interesting? 239A.14 What Are the Equivalence Classes of Adaptive Behavior? 240A.15 When Does Adaptation Lead to Optimization and Equilibrium? 241A.16 How Important Is Communication to Complex Adaptive Social Systems? 242A.17 How Do Decentralized Markets Equilibrate? 243A.18 When Do Organizations Arise? 243A.19 What Are the Origins of Social Life? 244B Practices for Computational Modeling 245B.1 Keep the Model Simple 246B.2 Focus on the Science, Not the Computer 246B.3 The Old Computer Test 247B.4 Avoid Black Boxes 247B.5 Nest Your Models 248B.6 Have Tunable Dials 248B.7 Construct Flexible Frameworks 249B.8 Create Multiple Implementations 249B.9 Check the Parameters 250B.10 Document Code 250B.11 Know the Source of Random Numbers 251B.12 Beware of Debugging Bias 251B.13 Write Good Code 251B.14 Avoid False Precision 252B.15 Distribute Your Code 253B.16 Keep a Lab Notebook 253B.17 Prove Your Results 253B.18 Reward the Right Things 254Bibliography 255Index 261show more

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