Normal view MARC view

Complex adaptive systems: an introduction to computational models of social life

Author: Miller, John H. ; Page, Scott E. Series: Princeton studies in complexity Publisher: Princeton University Press, 2007.Language: EnglishDescription: 263 p. : Graphs/Ill./Maps/Photos ; 24 cm.ISBN: 9780691127026Type of document: BookBibliography/Index: Includes bibliographical references and index
Tags: No tags from this library for this title. Log in to add tags.
Item type Current location Collection Call number Status Date due Barcode Item holds
Book Europe Campus
Main Collection
Print HB135 .M55 2007
(Browse shelf)
001236201
Available 001236201
Book Middle East Campus
Main Collection
Print HB135 .M55 2007
(Browse shelf)
500007127
Available 500007127
Total holds: 0

Includes bibliographical references and index

Digitized

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

There are no comments for this item.

Log in to your account to post a comment.
Koha 18.11 - INSEAD Catalogue
Home | Contact Us | What's Koha?