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University of Michigan-Ann Arbor
One of the real strengths of this book is its broad engagement with the sociological literature. The authors show how systems thinking underpins much sociological inquiry, from Marx and Durkheim to Parsons and Merton. This chapter would make a nice stand-alone introduction to foundational sociology. The authors also provide an impressive overview of the concepts and methods used in SACS, and the evolution of literature in this area. Given how much this area has grown in the past ten years, it is extremely useful to have the developments summarized in this way. The authors go into considerable detail on the art of "model building", which is useful for someone just learning the approach. Where the book is less successful, however, is when it attempts to apply the methods used in SACS research to the SACS community itself. For a book aimed at newcomers to the area of complexity research, the discussion is far too self-referential to appeal to a reader simply trying to get a toehold in the area, and figure out whether it will be useful for a given problem. Moreover, there is no clearly defined substantive research question being tackled in the analysis of SACS; the questions used to motivate the analysis are more focused on the uniqueness of the system under investigation. This is fine as far as it goes, but not the most convincing way of demonstrating the utility of the SACS approach. It is sometimes hard to follow the logic of the approach.
However, there is a lot to like about this book. It provides an in-depth summary of research in the area of SACS over the past ten years. It also does a very nice job of tracing out the evolution of this field, and its antecedents in other areas of research. It may be best suited to someone already familiar with the techniques of SACS, but interested in learning more about the area and its development.
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© Copyright Journal of Artificial Societies and Social Simulation, 2010