Miller and Page have written an excellent, very accessible introduction to complex systems as applied to social phenomena. They are especially careful in discussing inferences based on computer simulations. Since any computer simulation is an extreme reduction of real social interactions, it's necessary to be careful that a comparison between two simulations captures a principled difference. For example, Miller & Page describe two versions of the forest fire model. In the first version all trees are likely to grow in an available spot with the same probability, while in the second version the trees can have individual inhieritable probabilities of growth. The difference in principle is a differene in the basic abilities of the individual agents (trees). In this example the average number of trees is significantly higher in the second case.

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.

The authors do an excellent job of introducing the field to an educated audience. Any one who has a general college level education can read and understand the basics after reading the book. Tables and charts succinctly illustrate points Miller and Page make and illucidate the text.

If you are looking for a book that discusses progamming, how to do, or other deeper aspects of the field, you will be disappointed. However, if you are just curious and want a good general introduction to the field, perhaps with the goal of further exploration, it is a good anchor from which to base your learning.

Sometime I encounter books that are extremely important, that give me an appreciation for a knowledge domain I do not know enough about, and that I simply cannot read and review.

This book, and Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton Studies in Complexity) are two such books. I got half-way through this one, did the introduction to the other, from which I was immediately grabbed by the concept of:

"instead of explaining it, can you grow it?"

Howard Bloom, in Global Brain: The Evolution of Mass Mind from the Big Bang to the 21st Century teaches us that the only way to create a sustainable peace in the Palestine region is to provide absolute security for an entire generation, and raise two whole generations, one on each side, from kindergarten on us, generations that do not consider "the other" to be "pigs and monkeys" by the age of five.

Similarly, the literature on wealth of networks and the fortune at the bottom of the pyramid is growing, and I am convinced that public intelligence (decision support, full disclosure, end of information asymmetries) is going to accomplish two things in the next twenty years:

1) Eradicate corruption and enforce the triple-bottom line

2) Elevate five billion poor by teaching them one cell call at a time so that they can create infinite stabilizing wealth.

See for example:
Infinite Wealth: A New World of Collaboration and Abundance in the Knowledge Era
The Wealth of Networks: How Social Production Transforms Markets and Freedom
Revolutionary Wealth: How it will be created and how it will change our lives
The Fortune at the Bottom of the Pyramid: Eradicating Poverty Through Profits (Wharton School Publishing Paperbacks)

So the very best thing I can say about this book is that I am glad I bought it, I am very glad to have a sense, however weak, of this important exploratory area, and now I know that I need a team of generative social scientists that can do complex modeling for peace and prosperity solutions.

See also, just published at Amazon and free online at Earth Intelligence Network, Collective Intelligence: Creating a Prosperous World at Peace

I urge one and all to become familiar with World Index of Social and Environmental Responsibility (WISER), as best I can tell that is the center of gravity for empowering individuals with deep knowledge of the true costs and many human rights abuses and other crimes that we support today for lack of knowledge. I also recommend the pioneering EarthGame work of Medard Gabel, at BigPictureSmallWorld.

I found this book to be a bit more technical than I expected. Very good reference book.

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.. The various branches of engineering (electrical, chemical, mechanical) are effective because they recreate in everyday life artificially controlled, non-complex, non-adaptive, environments that can directly apply the discoveries of physics and chemistry. This option is generally not open to most behavioral scientists, who rarely have the opportunity of ``engineering'' social institutions and cultures.

Miller and Page stress that complex systems cannot be properly modeled using the statistical and mathematical tools associated with differentiable manifolds and normal statistical distributions. Rather, complex phenomena exhibit power law behavior in which statistical distributions have "fat tails" that lead to considerable activity far from the distributions central tendency. A rather stunning example, discussed in Chapter 9, is the size distribution of wars in the world occurring between 1820 and 1943. When the number of deaths in a war (a good measure of the size of the war) is 10 to the power n, the number of wars with this size is about 2 x 3 to the power 7-n.

Miller and Page do a find job of making complexity analysis accessible to the non-expert, without overwhelming the reader with specialized aspects of agent-based modeling or dynamical systems. They provide an exciting stepping-off point for detailed studies in particular disciplines.

I am a purchasing agent who buys books for my faculty, and as far as I know, this faculty member is very impressed with this particular book.

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.