Description
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.
Chapter
2.5 Complex Social Worlds Redux
2.5.1 Questioning Complexity
3.2 A More Formal Approach to Modeling
3.3 Modeling Complex Systems
4.1 A Theory of Emergence
4.2 Beyond Disorganized Complexity
4.2.1 Feedback and Organized Complexity
Part III Computational Modeling
5.1.1 Physics Envy: A Pseudo-Freudian Analysis
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
6 Why Agent-Based Objects?
6.1 Flexibility versus Precision
6.5 Heterogeneous Agents and Asymmetry
6.7 Repeatable and Recoverable
6.10 Economic E. coli (E. coni?)
Part IV Models of Complex Adaptive Social Systems
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
7.3 Eight Folding into One
8 Complex Adaptive Social Systems in One Dimension
8.2 Social Cellular Automata
8.2.1 Socially Acceptable Rules
8.3.1 The Zen of Mistakes in Majority Rule
8.4.2 Computation at the Edge of Chaos
8.4.3 The Edge of Robustness
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
9.6.5 The Forest Fire Model Redux
9.6.6 Criticality in Social Systems
10.3 A Taxonomy of 2 × 2 Games
10.4 Games Theory: One Agent, Many Games
10.5 Evolving Communication
10.5.2 Furthering Communication
11 Some Fundamentals of Organizational Decision Making
11.1 Organizations and Boolean Functions
11.3 Do Organizations Just Find Solvable Problems?
12 Social Science in Between
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
Appendixes A An Open Agenda For Complex Adaptive Social Systems
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?
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.7 Construct Flexible Frameworks
B.8 Create Multiple Implementations
B.11 Know the Source of Random Numbers
B.12 Beware of Debugging Bias
B.14 Avoid False Precision
B.15 Distribute Your Code
B.18 Reward the Right Things
Complex Adaptive Systems
：An Introduction to Computational Models of Social Life
( Princeton Studies in Complexity )
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