Complex Adaptive Systems : : An Introduction to Computational Models of Social Life / / Scott Page, John H. Miller.

• Superior document: Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Backlist 2000-2013
• VerfasserIn: Miller, John H.,; Page, Scott,
• Place / Publishing House: Princeton, NJ : : Princeton University Press, , [2009]; ©2007
• Year of Publication: 2009
• Edition: Course Book
• Language: English
• Series: Princeton Studies in Complexity ; 17
• Physical Description: 1 online resource (288 p.) :; 18 halftones. 16 line illus. 42 tables.
• Other title: Frontmatter --; Contents --; Figures --; Tables --; Preface --; Part I. Introduction --; Part II. Preliminaries --; Part III. Computational Modeling --; Part IV. Models of Complex Adaptive Social Systems --; Part V. Conclusions --; Appendix A. An Open Agenda for Complex Adaptive Social Systems --; Appendix B. Practices for Computational Modeling --; Bibliography --; Index
• Format: Mode of access: Internet via World Wide Web.
• ISBN: 9781400835522; 9783110442502
• DOI: 10.1515/9781400835522
• Access: restricted access
• Hierarchical level: Monograph
• Statement of Responsibility: Scott Page, John H. Miller.

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.