Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Anglais) Broché – 17 avril 2007
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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.
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. But 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 that we are still waiting for the canonical introductory 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.
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.
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.
Part IV develops ideas about modeling social systems. It primarily covers cellular automata without relying on heavy mathematics. While this is a necessary starting point to introduce some important concepts such as self-organized criticality and power-law phenomena (p. 165), cellular automata is a fairly limited approach to modeling human behavior and the book doesn't go much beyond this type of modeling to explain more sophisticated methods. In addition, most human and organizational behaviors don't follow power laws very closely, so these descriptions are informative but can be misleading. However, the authors correctly emphasize that human behavior is characteristically "fat-tailed" which is contrary to common misconceptions that (average) behavior is primarily Gaussian (normally distributed) in nature.
Chapter 7 introduces an interesting but seemingly arbitrary framework of the Buddhist "Eightfold Way". This appears to be a forced rather than a natural fit to how agents act in organizations and is puzzling for its inclusion. Yet, I may be missing something obvious here. So it would seem to be helpful for the authors to better connect this with the rest of the book (or leave it out entirely). The next section moves immediately to a discussion of modeling forest fires, so at least a summary or transition would be helpful.
Chapter 9 includes some interesting, albeit too brief, discussion of criticality in social systems (p. 177). Only one page is devoted to this topic. In contrast, nine pages were devoted to the "Eight-fold Way". Yet criticality in social systems seems to be the primary reason that one would study complexity in the first place. Hopefully, the authors will consider a revision of this book with some improved organization and a much expanded treatment of criticality.
Overall, the authors introduce and effectively define numerous complexity constructs that apply directly to individuals and organizations. This makes the book relatively unique and valuable, separate from its focus on agent-based modeling. Perhaps the modeling component is less useful in practice because the authors posit that only very simple models can be readily validated and used for most real-life problems. Yet, these core concepts are a necessary starting point for any type of agent based modelling initiative. Consequently, I recommend this book to anyone working in this area.
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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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