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Paperback
Publisher: Princeton University Press
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.
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| Well written but could be better organized |
| Customer Rating: 4 out of 5 |
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This book is about computational models of adaptive complex systems that primarily emerge through social organization, for example voter dynamics, population clustering, bank runs... It starts with more elementary less adaptive models and builds on them to show how emergent properties can be seen and adaptive behaviour can be superior to deducible deterministic optimal points. The book is quite dense and cant be rushed through, but is readible and essentially an invitation to people not currently using the techniques of recursive system techniques in multi-agent based models.
I found this book very readable and the writing style very engaging. The authors ability to keep the subject both intuitive as well as rigorous is quite unique and I rarely read book that are as well balanced. The approach is generally to force people to look at repurcussions and then think about the dynamics that brings them about, which is a lot more sensible than working through from initial conditions the evolution of nonlinear dynamical systems. This approach is contained to examples where one builds the examples and interactive dynamics of the agents themselves rather than for arbitrary chaotic systems.
This book though is not 5 stars to me as I dont like the way it was organized. The beginning of the book was hard for me to figure out what they were talking about or who they were trying to convince. The writing was good, but I was unable to gain insight into the systems they eventually were leading the reader to consider. I finally understood what they were talking about when they mentioned sugarworld which I was familiar with. At that point, in hindsight the beginning of the book made more sense. All in all my only criticism is the conclusion type arguments about the utility of the methods before discussing an elementary example was probably unecessary. I think it would have been better start to finish by starting with examples, building up the difficulty (which they did, but just a fair way into the book) and really reinforcing the merit of the approach (which i found self revealing) at the end rather than the beginning.
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| Conceptually rich but unnecessarily complicated |
| Customer Rating: 3 out of 5 |
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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 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.
Insofar as clarity and accessibility are concerned, however, I find myself in disagreement with the book's blurbs. Much of the mathematical formalism has been expunged from the discussions, yes, but that by itself does not guarantee enhanced communicability. The logic of the arguments, which in this field is considerable, must now be conveyed by other means, either verbal or visual. The authors do make an effort to explain in words the basic concepts when they begin a new topic. But when they proceed to discuss an actual model, they shift gears. Instead of explaining or illustrating in detail the model's functional intricacies, they switch to summarizing their findings and present a table or figure that encapsulates the model's results. Repeated readings of the text are almost always necessary in these cases, but understanding does not always automatically ensue. This approach does not appear to contribute to the goal of making the models "as simple and accessible as possible."
This situation is not due to writer's oversight but to a deliberate choice. Prior to discussing their first example model (a computational version of Tiebout's model), the authors state: "Rather than fully pursuing the detailed version of the model we just outlined ... here we provide just an overview." Fateful words which amount to an announcement of their modus operandi, as the subsequent instances demonstrate. Caveat lector. The reader is also assumed to possess a working knowledge of such things as game theory, elementary combinatorics, and statistics, among others. So brush up on the basics and stay close to a search engine.
Reading this book takes time and some effort; it is not a breezy read. One never gets to see an actual piece of code or even pseudocode, which one would normally expect in an introductory book on computational modeling. The reader is left in a vacuum as to the mechanics of implementation. Still, it is a good book in terms of its conceptual content. However, the inconsistency between the stated aim of providing clarity of exposition at an introductory level and the actual product the reader interacts with detracts from the book's overall quality. It seems like we are still waiting for the canonical text on complex adaptive social systems.
Note: If you are looking for a general overview of complexity theory intended for a lay audience, I would suggest Melanie Mitchell's Complexity: A Guided Tour. It is excellent. At the other end of the spectrum, if you're heavily into power math, consider Complex and Adaptive Dynamical Systems: A Primer (Springer Complexity) by Claudius Gros. It is rigorous.
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| complex systems applied to society |
| Customer Rating: 5 out of 5 |
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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.
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| very good introduction to the subject |
| Customer Rating: 5 out of 5 |
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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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| Good Overview |
| Customer Rating: 4 out of 5 |
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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.
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