To do science is to find patterns, and scientists are always looking for p- terns that they can use to structure their thinking about the world around them. Patterns are found in data, which is why science is inevitably a qu- titative study. But there is a difficulty in finding stable patterns in the data since many patterns are temporary phenomena that have occurred r- domly, and highly sophisticated empirical methods are necessary to dist- guish stable patterns from temporary or random patterns. When a scientist thinks he has found a stable pattern, he will generally try to capture that pattern in a model or theory. A theory is essentially a pattern, and thus theory is a central part of science. It would be nice to have a single pattern - a unified theory - that could serve as a map relating our understanding with the physical world around us. But the physical world has proven far too complicated for a single map, and instead we have had to develop smaller sub maps that relate to small areas of the physical world around us. This multiple theory approach presents the pr- lem of deciding not only what the appropriate map for the particular issue is, but also of handling the map overlays where different maps relate to overlapping areas of reality. It is not only science that is focused on finding patterns; so too are most individuals.
This volume extends the complexity approach to economics. This complexity approach is not a completely new way of doing economics, and that it is a replacement for existing economics, but rather the integration of some new analytic and computational techniques into economists’ bag of tools. It provides some alternative pattern generators, which can supplement existing approaches by providing an alternative way of finding patterns than be obtained by the traditional scientific approach. On this new kind of policy hints can be obtained.
The reason why the complexity approach is taking hold now in economics is because the computing technology has advanced. This advance allows consideration of analytical systems that could not previously be considered by economists. Consideration of these systems suggested that the results of the "control-based" models might not extend easily to more complicated systems, and that we now have a method―piggybacking computer assisted analysis onto analytic methods―to start generating patterns that might provide a supplement to the standard approach. It is that approach that we consider the complexity approach.
The papers in this volume develop these ideas. In terms of policy the papers suggest that economists should become a bit less certain in their policy conclusions, and that they expand their bag of tools supplementing their standard model with some additional models including (1) agent based models, in which one does not use analytics to develop the pattern, but instead one uses computational power to deal with specification of models that are far beyond analytic solution; and (2) non-linear dynamic stochastic models many of which are beyond analytic solution, but whose nature can be discovered by a combination of analytics and computer simulations.
The volume is divided into four sections: general issues, modeling issues, applications, and policy issues. Each struggles with complicated ideas relatedto our general theme, and a number of them try out new techniques.
