UID:
edocfu_9959227315302883
Format:
1 online resource (284 p.)
Edition:
Course Book
ISBN:
1-282-45811-6
,
1-282-93635-2
,
9786612458118
,
9786612936357
,
1-4008-3552-6
,
0-691-12702-6
Series Statement:
Princeton studies in complexity
Content:
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.
Note:
Description based upon print version of record.
,
pt. 1. INTRODUCTION. Introduction -- Complexity in social worlds -- pt. 2. PRELIMINARIES. Modeling -- On emergence -- pt. 3. COMPUTATIONAL MODELING. Computation as theory -- Why agent-based objects? -- pt. 4. MODELS OF COMPLEX ADAPTIVE SOCIAL SYSTEMS. A basic framework -- Complex adaptive social systems in one dimension -- Social dynamics -- Evolving automata -- Some fundamentals of organizational decision making -- pt. 5. CONCLUSIONS. Social science in between -- Epilogue -- Appendixes. A. An open agenda for complex adaptive social systems -- B. Practices for computational modeling.
,
Issued also in print.
,
English
Additional Edition:
ISBN 0-691-13096-5
Language:
English
DOI:
10.1515/9781400835522
