| 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 |
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