Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Studies in Complexity)

Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Studies in Complexity)

By: John H. Miller   Scott E. Page  

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Average Customer Rating: 4.5 out of 5

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.

Publisher: Princeton University Press

Customer Review: 4 out of 5
The relevance of complexity theory - The book starts with a traditional approach to the game theory, in particular about the work of Morgenstern-von Neumann. This model must be related to the chaos theory for the possibility of an application to the social dynamics. The simulation of a structure based by agent can be seen also in an informatic context. The auctors adfirm that it exists different parts of human thinking able to influence the behaviour of people.

Customer Review: 5 out of 5
A much needed book - This is an excellent book that introduces the reader to the concept of computational models of complex adaptive systems. The language used is both simple and engaging. The authors discuss why modeling is used, and more importantly, what are its limitations. This book will not teach you to model complex adaptive systems. Instead, it will give you the knowledge necessary to appreciate the intuition behind modeling complex systems. Many of the ideas introduced are complex but the authors' writing style makes them easy to understand. The best thing about the book was the choice of examples. Although simple, each example was able to convey certain ideas that greatly enhance the reader's understanding. Anyone interested in modeling complex systems should read this book before any other.

Customer Review: 5 out of 5
This book is a great way to be introduced to the field - This textbook aims at introducing a relatively new and emerging scientific approach, complex adaptive systems. Simply stated, we're talking about complex systems in the sense that their dynamic is far too complex or chaotic to be modeled using analytical equations. Examples of such systems are social phenomena including immigration patterns and segregation, biological patterns such as bees behavior and others. The underlying assumption is that the system under review is too complex to be modeled using mathematical tools, and/or are too complex to conduct laboratory experiments with. The new method introduced in this book talks about modeling a very simple system of interacting entities (agents), with very simple micro behavior rules, letting the system run and observing the emerging macro-behavior of the system as a whole.

The book is a great textbook. Its flow of topics is in the correct order to taking the reader from the problem of why this approach is needed, through talking openly about the widespread criticism of this approach and tries answering it in a logical and intelligent way. It then continues to explaining what is a model and how to construct one and off to some examples that show other important corner stones of the field. I couldn't ask for a better arrangement of such book. The book is relatively easy to follow and can be used as an undergraduate textbook or for researchers who look for a good introduction to the field.

Some minor problems that I stumbled upon while reading are as follow: (1) chapter 5 is extremely important as it tries to discuss the approach's criticism, however the arguments wasn't always convincing. Specifically, I would like to see some examples of problems X that are given to the neoclassical theorists, and see some discussions on their inability to deal with them and how this approach can cope with them. (2) The research problems that are introduced are very simple (as also stated by the authors themselves), I think that another chapter with two or three examples of real problems would make this book more valuable for the more knowledgeable readers (e.g. some of Epstein works). (3) After doing a lot of reading on that topic I am still amazed to find new terminology to similar ideas I think the field will mature and be more comprehensive to newcomers if the terminology will be standardize.

Overall, this book provides a great introduction to the field, easy to follow, great arrangement of topics. Highly recommended.

Customer Review: 4 out of 5
Well written but could be better organized - 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.

Customer Review: 3 out of 5
Conceptually rich but unnecessarily complicated - 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.

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 required, but understanding does not necessarily 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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