Complex Adaptive Systems

An Introduction to Computational Models of Social Life

By: John H. Miller, Scott Page

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Paperback | 5 June 2007

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288 Pages

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23.4 x 15.2 x 1.6

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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 transcended.

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"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 Diversity

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List of Figures	p. xiii
List of Tables	p. xv
Preface	p. xvii
Introduction	p. 1
Introduction	p. 3
Complexity in Social Worlds	p. 9
The Standing Ovation Problem	p. 10
What's the Buzz?	p. 14
Stay Cool	p. 14
Attack of the Killer Bees	p. 15
Averaging Out Average Behavior	p. 16
A Tale of Two Cities	p. 17
Adding Complexity	p. 20
New Directions	p. 26
Complex Social Worlds Redux	p. 27
Questioning Complexity	p. 27
Preliminaries	p. 33
Modeling	p. 35
Models as Maps	p. 36
A More Formal Approach to Modeling	p. 38
Modeling Complex Systems	p. 40
Modeling Modeling	p. 42
On Emergence	p. 44
A Theory of Emergence	p. 46
Beyond Disorganized Complexity	p. 48
Feedback and Organized Complexity	p. 50
Computational Modeling	p. 55
Computation as Theory	p. 57
Theory versus Tools	p. 59
Physics Envy: A Pseudo-Freudian Analysis	p. 62
Computation and Theory	p. 64
Computation in Theory	p. 64
Computation as Theory	p. 67
Objections to Computation as Theory	p. 68
Computations Build in Their Results	p. 69
Computations Lack Discipline	p. 70
Computational Models Are Only Approximations to Specific Circumstances	p. 71
Computational Models Are Brittle	p. 72
Computational Models Are Hard to Test	p. 73
Computational Models Are Hard to Understand	p. 76
New Directions	p. 76
Why Agent-Based Objects?	p. 78
Flexibility versus Precision	p. 78
Process Oriented	p. 80
Adaptive Agents	p. 81
Inherently Dynamic	p. 83
Heterogeneous Agents and Asymmetry	p. 84
Scalability	p. 85
Repeatable and Recoverable	p. 86
Constructive	p. 86
Low Cost	p. 87
Economic E. coli (E. coni?)	p. 88
Models of Complex Adaptive Social Systems	p. 91
A Basic Framework	p. 93
The Eightfold Way	p. 93
Right View	p. 94
Right Intention	p. 95
Right Speech	p. 96
Right Action	p. 96
Right Livelihood	p. 97
Right Effort	p. 98
Right Mindfulness	p. 100
Right Concentration	p. 101
Smoke and Mirrors: The Forest Fire Model	p. 102
A Simple Model of Forest Fires	p. 102
Fixed, Homogeneous Rules	p. 102
Homogeneous Adaptation	p. 104
Heterogeneous Adaptation	p. 105
Adding More Intelligence: Internal Models	p. 107
Omniscient Closure	p. 108
Banks	p. 109
Eight Folding into One	p. 110
Conclusion	p. 113
Complex Adaptive Social Systems in One Dimension	p. 114
Cellular Automata	p. 115
Social Cellular Automata	p. 119
Socially Acceptable Rules	p. 120
Majority Rules	p. 124
The Zen of Mistakes in Majority Rule	p. 128
The Edge of Chaos	p. 129
Is There an Edge?	p. 130
Computation at the Edge of Chaos	p. 137
The Edge of Robustness	p. 139
Social Dynamics	p. 141
A Roving Agent	p. 141
Segregation	p. 143
The Beach Problem	p. 146
City Formation	p. 151
Networks	p. 154
Majority Rule and Network Structures	p. 158
Schelling's Segregation Model and Network Structures	p. 163
Self-Organized Criticality and Power Laws	p. 165
The Sand Pile Model	p. 167
A Minimalist Sand Pile	p. 169
Fat-Tailed Avalanches	p. 171
Purposive Agents	p. 175
The Forest Fire Model Redux	p. 176
Criticality in Social Systems	p. 177
Evolving Automata	p. 178
Agent Behavior	p. 178
Adaptation	p. 180
A Taxonomy of 2 x 2 Games	p. 185
Methodology	p. 187
Results	p. 189
Games Theory: One Agent, Many Games	p. 191
Evolving Communication	p. 192
Results	p. 194
Furthering Communication	p. 197
The Full Monty	p. 198
Some Fundamentals of Organizational Decision Making	p. 200
Organizations and Boolean Functions	p. 201
Some Results	p. 203
Do Organizations Just Find Solvable Problems?	p. 206
Imperfection	p. 207
Future Directions	p. 210
Conclusions	p. 211
Social Science in Between	p. 213
Some Contributions	p. 214
The Interest in Between	p. 218
In between Simple and Strategic Behavior	p. 219
In between Pairs and Infinities of Agents	p. 221
In between Equilibrium and Chaos	p. 222
In between Richness and Rigor	p. 223
In between Anarchy and Control	p. 225
Here Be Dragons	p. 225
Epilogue	p. 227
Interest in Between	p. 227
Social Complexity	p. 228
The Faraway Nearby	p. 230
Appendixes
An Open Agenda For Complex Adaptive Social Systems	p. 231
Whither Complexity	p. 231
What Does it Take for a System to Exhibit Complex Behavior?	p. 233
Is There an Objective Basis for Recognizing Emergence and Complexity?	p. 233
Is There a Mathematics of Complex Adaptive Social Systems?	p. 234
What Mechanisms Exist for Tuning the Performance of Complex Systems?	p. 235
Do Productive Complex Systems Have Unusual Properties?	p. 235
Do Social Systems Become More Complex over Time	p. 236
What Makes a System Robust?	p. 236
Causality in Complex Systems?	p. 237
When Does Coevolution Work?	p. 237
When Does Updating Matter?	p. 238
When Does Heterogeneity Matter?	p. 238
How Sophisticated Must Agents Be Before They Are Interesting?	p. 239
What Are the Equivalence Classes of Adaptive Behavior?	p. 240
When Does Adaptation Lead to Optimization and Equilibrium?	p. 241
How Important Is Communication to Complex Adaptive Social Systems?	p. 242
How Do Decentralized Markets Equilibrate?	p. 243
When Do Organizations Arise?	p. 243
What Are the Origins of Social Life?	p. 244
Practices for Computational Modeling	p. 245
Keep the Model Simple	p. 246
Focus on the Science, Not the Computer	p. 246
The Old Computer Test	p. 247
Avoid Black Boxes	p. 247
Nest Your Models	p. 248
Have Tunable Dials	p. 248
Construct Flexible Frameworks	p. 249
Create Multiple Implementations	p. 249
Check the Parameters	p. 250
Document Code	p. 250
Know the Source of Random Numbers	p. 251
Beware of Debugging Bias	p. 251
Write Good Code	p. 251
Avoid False Precision	p. 252
Distribute Your Code	p. 253
Keep a Lab Notebook	p. 253
Prove Your Results	p. 253
Reward the Right Things	p. 254
Bibliography	p. 255
Index	p. 261
Table of Contents provided by Ingram. All Rights Reserved.

Complex Adaptive Systems

An Introduction to Computational Models of Social Life

At a Glance

Paperback

Industry Reviews

Shipping

How to return your order

Defective items

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