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More About This Textbook
Overview
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 transcended, allowing the investigation of systems composed of moderate numbers of interacting and thoughtful, but not perfect, agents across a variety of important domains. Finally, they outline a twenty-first-century research agenda for the social sciences based on these ideas.
About the Author:
John H. Miller is Professor of Economics and Social Sciences in the Department of Social and Decision Sciences at Carnegie Mellon University, and a research professor at the Santa Fe Institute
About theAuthor:
Scott E. Page is Professor of Complex Systems, Political Science, and Economics at the University of Michigan, and an external faculty member at the Santa Fe Institute
What People Are Saying
Arrow
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
Samuel Bowles
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"
Elinor Ostrom
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 Diversity"
Product Details
Related Subjects
Meet the Author
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).
Table of Contents
List of Figures xiii
List of Tables xv
Preface xvii
Part I: Introduction 1
Chapter 1: Introduction 3
Chapter 2: Complexity in Social Worlds 9
2.1 The Standing Ovation Problem 10
2.2 What's the Buzz? 14
2.2.1 Stay Cool 14
2.2.2 Attack of the Killer Bees 15
2.2.3 Averaging Out Average Behavior 16
2.3 A Tale of Two Cities 17
2.3.1 Adding Complexity 20
2.4 New Directions 26
2.5 Complex Social Worlds Redux 27
2.5.1 Questioning Complexity 27
Part II: Preliminaries 33
Chapter 3: Modeling 35
3.1 Models as Maps 36
3.2 A More Formal Approach to Modeling 38
3.3 Modeling Complex Systems 40
3.4 Modeling Modeling 42
Chapter 4: On Emergence 44
4.1 A Theory of Emergence 46
4.2 Beyond Disorganized Complexity 48
4.2.1 Feedback and Organized Complexity 50
Part III: Computational Modeling 55
Chapter 5: Computation as Theory 57
5.1 Theory versus Tools 59
5.1.1 Physics Envy: A Pseudo-Freudian Analysis 62
5.2 Computation and Theory 64
5.2.1 Computation in Theory 64
5.2.2 Computation as Theory 67
5.3 Objections to Computation as Theory 68
5.3.1 Computations Build in Their Results 69
5.3.2 Computations Lack Discipline 70
5.3.3 Computational Models Are Only Approximations to Specific Circumstances 71
5.3.4 Computational Models Are Brittle 72
5.3.5 Computational Models Are Hard to Test 73
5.3.6 Computational Models Are Hard to Understand 76
5.4 New Directions 76
Chapter 6: Why Agent-Based Objects? 78
6.1 Flexibility versus Precision 78
6.2 Process Oriented 80
6.3 Adaptive Agents 81
6.4 Inherently Dynamic 83
6.5 Heterogeneous Agents and Asymmetry 84
6.6 Scalability 85
6.7 Repeatable and Recoverable 86
6.8 Constructive 86
6.9 Low Cost 87
6.10 Economic E. coli (E. coni?) 88
Part IV: Models of Complex Adaptive Social Systems 91
Chapter 7: A Basic Framework 93
7.1 The Eightfold Way 93
7.1.1 Right View 94
7.1.2 Right Intention 95
7.1.3 Right Speech 96
7.1.4 Right Action 96
7.1.5 Right Livelihood 97
7.1.6 Right Effort 98
7.1.7 Right Mindfulness 100
7.1.8 Right Concentration 101
7.2 Smoke and Mirrors: The Forest Fire Model 102
7.2.1 A Simple Model of Forest Fires 102
7.2.2 Fixed, Homogeneous Rules 102
7.2.3 Homogeneous Adaptation 104
7.2.4 Heterogeneous Adaptation 105
7.2.5 Adding More Intelligence: Internal Models 107
7.2.6 Omniscient Closure 108
7.2.7 Banks 109
7.3 Eight Folding into One 110
7.4 Conclusion 113
Chapter 8: Complex Adaptive Social Systems in One Dimension 114
8.1 Cellular Automata 115
8.2 Social Cellular Automata 119
8.2.1 Socially Acceptable Rules 120
8.3 Majority Rules 124
8.3.1 The Zen of Mistakes in Majority Rule 128
8.4 The Edge of Chaos 129
8.4.1 Is There an Edge? 130
8.4.2 Computation at the Edge of Chaos 137
8.4.3 The Edge of Robustness 139
Chapter 9: Social Dynamics 141
9.1 A Roving Agent 141
9.2 Segregation 143
9.3 The Beach Problem 146
9.4 City Formation 151
9.5 Networks 154
9.5.1 Majority Rule and Network Structures 158
9.5.2 Schelling's Segregation Model and Network Structures 163
9.6 Self-Organized Criticality and Power Laws 165
9.6.1 The Sand Pile Model 167
9.6.2 A Minimalist Sand Pile 169
9.6.3 Fat-Tailed Avalanches 171
9.6.4 Purposive Agents 175
9.6.5 The Forest Fire Model Redux 176
9.6.6 Criticality in Social Systems 177
Chapter 10: Evolving Automata 178
10.1 Agent Behavior 178
10.2 Adaptation 180
10.3 A Taxonomy of 2 x 2 Games 185
10.3.1 Methodology 187
10.3.2 Results 189
10.4 Games Theory: One Agent, Many Games 191
10.5 Evolving Communication 192
10.5.1 Results 194
10.5.2 Furthering Communication 197
10.6 The Full Monty 198
Chapter 11: Some Fundamentals of Organizational Decision Making 200
11.1 Organizations and Boolean Functions 201
11.2 Some Results 203
11.3 Do Organizations Just Find Solvable Problems? 206
11.3.1 Imperfection 207
11.4 Future Directions 210
Part V: Conclusions 211
Chapter 12: Social Science in Between 213
12.1 Some Contributions 214
12.2 The Interest in Between 218
12.2.1 In between Simple and Strategic Behavior 219
12.2.2 In between Pairs and Infinities of Agents 221
12.2.3 In between Equilibrium and Chaos 222
12.2.4 In between Richness and Rigor 223
12.2.5 In between Anarchy and Control 225
12.3 Here Be Dragons 225
Epilogue 227
The Interest in Between 227
Social Complexity 228
The Faraway Nearby 230
Appendixes
A An Open Agenda For Complex Adaptive Social Systems 231
A.1 Whither Complexity 231
A.2 What Does it Take for a System to Exhibit Complex Behavior? 233
A.3 Is There an Objective Basis for Recognizing Emergence and Complexity? 233
A.4 Is There a Mathematics of Complex Adaptive Social Systems? 234
A.5 What Mechanisms Exist for Tuning the Performance of Complex Systems? 235
A.6 Do Productive Complex Systems Have Unusual Properties? 235
A.7 Do Social Systems Become More Complex over Time 236
A.8 What Makes a System Robust? 236
A.9 Causality in Complex Systems? 237
A.10 When Does Coevolution Work? 237
A.11 When Does Updating Matter? 238
A.12 When Does Heterogeneity Matter? 238
A.13 How Sophisticated Must Agents Be Before They Are Interesting? 239
A.14 What Are the Equivalence Classes of Adaptive Behavior? 240
A.15 When Does Adaptation Lead to Optimization and Equilibrium? 241
A.16 How Important Is Communication to Complex Adaptive Social Systems? 242
A.17 How Do Decentralized Markets Equilibrate? 243
A.18 When Do Organizations Arise? 243
A.19 What Are the Origins of Social Life? 244
B Practices for Computational Modeling 245
B.1 Keep the Model Simple 246
B.2 Focus on the Science, Not the Computer 246
B.3 The Old Computer Test 247
B.4 Avoid Black Boxes 247
B.5 Nest Your Models 248
B.6 Have Tunable Dials 248
B.7 Construct Flexible Frameworks 249
B.8 Create Multiple Implementations 249
B.9 Check the Parameters 250
B.10 Document Code 250
B.11 Know the Source of Random Numbers 251
B.12 Beware of Debugging Bias 251
B.13 Write Good Code 251
B.14 Avoid False Precision 252
B.15 Distribute Your Code 253
B.16 Keep a Lab Notebook 253
B.17 Prove Your Results 253
B.18 Reward the Right Things 254
Bibliography 255
Index 261