×

Uh-oh, it looks like your Internet Explorer is out of date.

For a better shopping experience, please upgrade now.

Complex Adaptive Systems: An Introduction to Computational Models of Social Life
     

Complex Adaptive Systems: An Introduction to Computational Models of Social Life

5.0 1
by John H. Miller
 

See All Formats & Editions

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

Overview

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.

Product Details

ISBN-13:
9781400835522
Publisher:
Princeton University Press
Publication date:
11/28/2009
Series:
Princeton Studies in Complexity
Sold by:
Barnes & Noble
Format:
NOOK Book
Pages:
288
Sales rank:
856,426
File size:
4 MB

What People are Saying About This

Arrow
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
Elinor Ostrom
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"
Samuel Bowles
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"

Meet the Author

John H. Miller is professor of economics and social sciences at Carnegie Mellon University. Scott E. Page is professor of complex systems, political science, and economics at the University of Michigan. He is the author of "The Difference" (Princeton).

Customer Reviews

Average Review:

Post to your social network

     

Most Helpful Customer Reviews

See all customer reviews

Complex Adaptive Systems: An Introduction to Computational Models of Social Life 5 out of 5 based on 0 ratings. 1 reviews.
Anonymous More than 1 year ago