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Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Studies in Complexity) Paperback – March 25, 2007

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Product Details

  • Series: Princeton Studies in Complexity
  • Paperback: 272 pages
  • Publisher: Princeton University Press (March 25, 2007)
  • Language: English
  • ISBN-10: 0691127026
  • ISBN-13: 978-0691127026
  • Product Dimensions: 0.8 x 6 x 9 inches
  • Shipping Weight: 15.2 ounces (View shipping rates and policies)
  • Average Customer Review: 4.1 out of 5 stars  See all reviews (25 customer reviews)
  • Amazon Best Sellers Rank: #111,599 in Books (See Top 100 in Books)

Editorial Reviews


"Shows that computational modeling is slowly beginning to take root in the social sciences." -- Philip Ball, Nature

From the Inside Flap

"The use of computational, especially agent-based, models has already shown its value in illuminating the study of economic and other social processes. Miller and Page have written an orientation to this field that is a model of motivation and insight, making clear the underlying thinking and illustrating it by varied and thoughtful examples. It conveys with remarkable clarity the essentials of the complex systems approach to the embarking researcher."--Kenneth J. Arrow, winner of the Nobel Prize in economics

"In Complex Adaptive Systems, two masters of this burgeoning field provide a highly readable and novel restatement of the logic of social interactions, linking individually based micro processes to macrosocial outcomes, ranging from Adam Smith's invisible hand to Thomas Schelling's models of standing ovations. The book combines the vision of a new Santa Fe school of computational, social, and behavioral science with essential 'how to' advice for apprentice modelers."--Samuel Bowles, author of Microeconomics: Behavior, Institutions, Evolution

"This is a wonderful book that will be read by graduate students, faculty, and policymakers. The authors write in an extraordinarily clear manner about topics that are very technical and difficult for many people. I sat down to begin thumbing through and found myself deeply engaged."--Elinor Ostrom, author of Understanding Institutional Diversity

--This text refers to an out of print or unavailable edition of this title.

Customer Reviews

I found this book very readable and the writing style very engaging.
A. Menon
This is an excellent book that introduces the reader to the concept of computational models of complex adaptive systems.
N. Mozahem
I would definitely recommend this book to any one like myself that are looking to explore this fascinating subject.
Geoff Howard

Most Helpful Customer Reviews

113 of 117 people found the following review helpful By Vincent Matossian on August 3, 2007
Format: Paperback
At the time of writing this review, this book isn't searchable through Amazon, that's too bad because if you're reading the reviews wondering if it's worth buying, just browsing through any page from the intro or appendix B would clearly resolve any remnant hesitation. This book is a must have for anyone even remotely interested in complex adaptive systems. Scott Page and John Miller dress the landscape and state of the art of computational social science, the issues are motivated from the ground up and the existing approaches to resolve them explicitly detailed, yet using clear and jargon free language. For example, descriptions of the many concepts repeatedly used in the scientific method (of CAS et al) such as ergodicity or optimization theory are refreshing and insightful, simply stuff you don't get from textbooks, but rather that one would learn over years of experience doing.

In summary, the authors are handing us an expert summary of literature and developments of a complex field in a concise, fun and delightful read, it would be a shame to miss it.
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110 of 115 people found the following review helpful By Diego Azeta on July 10, 2009
Format: Paperback
Complexity is a hot subject. Unfortunately, the language of dynamical systems theory is advanced mathematics, which means that most of the available literature is not readily accessible to lay readers. Educated nonspecialists are left with few options aside from the occasional overview which, typically, does not delve too deeply into the subject matter. Given this state of affairs, Miller and Page's book would seem to be a godsend.

A stated aim of the book is that of providing a "clear, comprehensive, and accessible account of complex adaptive social systems" for "both academics and the sophisticated lay reader." Insofar as comprehensiveness, the authors deliver. Readers are first offered preliminary discussions on complexity in social worlds, modeling, and emergence, followed by a more detailed treatment of computational modeling as a tool for theory development and of agent-based objects as the recommended means to explore complex adaptive social systems. Then a basic framework of agent-based systems is presented, followed by discussions of unidimensional complexity models and the edge of chaos, social dynamics, evolving automata, and organizational decision making. These topics are largely illustrated with the authors' previously published models. Finally, conclusions are derived regarding the book's central theme: the "interest in between" as it pertains to complex social systems (which tend to fall in between the usual scientific boundaries). Two appendices bring up the rear: an agenda for future research in complex systems and an outline of best practices for computational modeling. The thematic coverage is ample and varied, excellent for a general introductory work on social complexity.
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68 of 75 people found the following review helpful By Herbert Gintis on December 1, 2007
Format: Paperback Verified Purchase
Living systems are generally complex, dynamic adaptive systems with emergent properties that analytical models attending only to the local interactions of the system fail to capture. We must complement the standard analytical methods of physics, biology, and economics by additional mathematical tools, such as agent-based simulation and network theory.

A complex system consists of a large population of similar entities (e.g., human individuals) who interact through regularized channels (e.g., networks, markets, social institutions) with significant stochastic elements, without a system of centralized organization and control (i.e., if there is a state, it controls only a fraction of all social interactions, and itself is a complex system). A complex system is adaptive if it evolves through some evolutionary (genetic, cultural, agent-based silicon, or other) process of hereditary reproduction, mutation, and selection.. Characterizing a system as complex adaptive does not explain its operation, and does not solve any problems. However, it suggests that certain modeling tools are likely to be effective that have little use in a non-complex system.

Such novel research tools are needed because a complex adaptive system generally has emergent properties that cannot be analytically derived from its component parts. The stunning success of modern physics and chemistry lies in their ability to avoid or strictly limit emergence. Indeed, the experimental method in natural science is to create highly simplified laboratory conditions, under which modeling becomes analytically tractable. Physics is no more effective than economics or biology in analyzing complex real-world phenomena in situ..
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12 of 13 people found the following review helpful By F. Barbieri on July 23, 2008
Format: Paperback
A nice introduction material. You will learn how complex phenomena are currently studied . I will use this book as an intro material to complex systems in my economics course.
My only complain is that the book scarcelly discuss aplications in social sciences!!! I have to use specific articles with applications for that. the author should supress the subtitle. but it is still an excellent book.
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6 of 6 people found the following review helpful By William A. Reed on September 28, 2013
Format: Paperback Verified Purchase
This is a unique and valuable book on complex adaptive systems which is focused specifically on the organizational context. Although Miller & Page describe their book as "an in introduction to computational models of social life" the general interest reader should appreciate its thorough and accessible discussion of complex systems without regard to the modeling aspect.

Part I begins with simple examples of complexity and depicts how emergence can stem from the interaction of multiple agents acting semi-autonomously using simple rules. The theme is developed that individual agents (actors) form complex systems when they are interdependent in some way and these systems can generate complex and unpredictable behaviors without the benefit of a central controller. This leads to a brief but important discussion of some counter-intuitive characteristics of complex systems. For example, "adding noise to the system may actually enhance the ability of a system to find superior outcomes" (p. 30). Several examples make these ideas easy to understand and provide the groundwork for introducing agent-based modeling in Part II.

In Part II, chapter 4 renders the important construct of "emergence" which is the defining characteristic of complex adaptive systems. The authors offer an excellent definition of emergence as "individual, localized behavior [that] aggregates into global behavior that is, in some sense, disconnected from its origins" (p. 44).

Chapter 5 (Part III) begins the detailed discussion of agent-based modeling and computation as a theoretical approach to understanding complex systems. Agent-based models are said to have the capacity to produce "surprising results" (p. 67) because of the interaction of numerous random and non-linear combinations of variables.
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