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Strategic Organization Design Unit, Department of Marketing and Management, University of Southern Denmark
It is well known that complexity research has greatly benefitted from new mathematical tools that were developed and refined over the last few decades. Rather than studying equilibrium behavior, complexity research focuses on the much more challenging analysis of disequilibrium phenomena, including the characterization of possible paths and transient effects. It is obvious that liberalization of markets and technological progress have increased complexity and turbulence in the economy, thereby inviting application of the new tools in complexity research. These include discrete mathematics as well as computational methods.
The present handbook opens with a brief introductory chapter by J. Barkley Rosser, Jr. and then offers three overview chapters that take the reader through the conceptual developments that have moved complexity research from periphery towards the center in economic research (by W. Brian Arthur, J. Barkley Rosser, Jr., and K. Vela Velupillai). Not all of these chapters are an entirely easy read, but they are quite rewarding for the patient reader.
As the chapter on oligopoly dynamics by Michael Kopel makes clear, the last decades have seen huge advances in the study of complex phenomena that come about through mutually adapting firms. It is a chapter ripe with inspiration and suggestions for future research.
The chapter by Cars H. Hommes elaborates on the contrasting behavioral assumptions made by Herbert Simon and his main stream progeny, John Muth and Robert Lucas. The idea of rational expectations provides a powerful and elegant shortcut to traversing the problem of deriving macro-behavior from individual agents that harbor heterogeneous beliefs, but it has become clear that valuable insight is lost in the process. By contrast, the assumption of bounded rationality allows a much more detailed (and messy) study of how aggregation processes play out in the economy. Chapter seven by Alan Kirman elaborates on that argument and chapter eight by Richard H. Day gives a nice overview of the conceptual tools and methods for the study of macro-economic complexity.
Alan Kirman's chapter provides a good overview of the problem of aggregation, and an explanation why this problem is of fundamental nature in economic theory. If we sidestep the problem of aggregation, complexity research does not make much sense. But embracing it undermines modern macroeconomic theory. In view of the limited success of understanding aggregate economic behavior (witness the near-collapse of the financial markets in 2008), it seems reasonable to promote a complexity approach which attributes less rationality to agents and more importance to the way their interactions are structured. In that regard, complexity research and evolutionary approaches associated with Richard R. Nelson and Sidney G. Winter seem complementary. It is therefore curious that the Handbook's two chapters on evolutionary and ecological-environmental economics (respectively, by Herbert Gintis, Ross Cressman and Thijs Ruijgrok and by J. Barkley Rosser, Jr.) do not make this connection.
In addition to the previously mentioned chapters, there are sections on econophysics and financial markets (two chapters, respectively by Thomas Lux and Joseph L. McCauley, Kevin E. Bassler and Gemunu H. Gunaratne) and international economics (two chapters, respectively by Frank H. Westerhoff and Hans-Peter Brunner and Peter Allen). The book concludes with two chapters on the broader historical perspectives, one on Austrian economics by Roger Koppl, and the other, final, chapter on complexity research in the history of economic thought, by David Colander. This last chapter makes the intriguing observation that Charles Babbage and John von Neumann are two economic thinkers who are moving up from footnotes to the main text. It is notably, von Neumann's work on self-replicating automata that is mentioned in the context of complexity research.
In sum, the book provides comprehensive coverage of complexity research in economics. Some of the chapters are easily accessible, but most require prior interest or background. The book is not an entirely easy read, but quite rewarding for the patient reader. As a handbook on complexity in economic research, there are also curious oversights. As previously mentioned, it would be obvious to include the evolutionary approaches associated with Richard R. Nelson and Sidney G. Winter. Another omission is the work on behavioral economics and organization theory initiated by James G. March. In later instantiations of this approach, Daniel A. Levinthal has introduced complexity research (NK-models) as a highly influential approach to the study of organizational learning and adaptation. Despite some holes in the intellectual canvas, this book offers a highly inspiring set of papers on complexity research. There is much to take away.
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© Copyright Journal of Artificial Societies and Social Simulation, 2011