Economists have begun to use complex systems as the basis for formal modeling precisely in order to understand certain aggregate features of environments which are characterized by many heterogeneous actors. Examples are easy to construct. A stock market is comprised of many traders with idiosyncratic beliefs about the future. The actors exchange information and react to some types of common information, but stock prices are ultimately determined by a large number of decentralized buy and sell decisions. In a very different area, the composition of residential communities is determined by the individual preferences and location decisions of individual families.
Notice that in each case, there is a feedback between the aggregate characteristics of the economic environment under analysis and the individual actors which comprise that environment. Movements in stock prices influence the beliefs of individual traders and in turn influence their subsequent decisions. Families make community choices on the basis of expectations concerning community characteristics; these characteristics in turn evolve in response to individual choices. These feedbacks are in and of themselves not unique to complex environments. After all, elementary economics teaches us about the feedback between individual demand and supply decisions and market level prices.
Complexity deepens our understanding of economic phenomena by illustrating how various types of microeconomic structures lead to particular aggregate economic phenomena. For example, as illustrated in work by W. Brian Arthur, John Holland, Blake LeBaron, and Richard Palmer [1] booms and crashes in prices appears to be a frequent feature of stock market environments in which evolving rules-of-thumb behavior interact to determine individual purchasing decisions. Similarly, the emergence of racially segregated neighborhoods from a collection of individuals with different preferences for community racial composition has been illustrated by Thomas Schelling [2], in what is probably the first paper on economic complexity. Hence, the value of complex system thinking in the social sciences, for either researchers or policymakers, lies in its potential for enriching our understanding of the relationships between aggregate outcomes and individual decisions. While plausible theoretical complexity-based models have been developed to explain phenomena ranging from out of wedlock births (William Brock and Steven Durlauf) [3] to the distribution of city sizes (Paul Krugman) [4], these models have not been subjected to sufficient empirical scrutiny to assess their validity. In particular, the causal mechanisms which drive these models have yet to be shown to be empirically salient.">
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This essay is written with two goals. The first is to outline the main ideas underlying the growing study of complex economic environments. The second is to suggest areas of public policy where those ideas might be important. Both goals are necessarily speculative. The study of complex systems, whether natural or social, is still in its infancy. While many insights and plausible conjectures have been generated, the long term importance of this work is still unclear.
In formal analyses, complexity denotes something quite different from merely "complicated" or "hard to analyze or solve." For our purposes, a system is said to be complex when it exhibits some type of order as a result of the interactions of many heterogeneous objects. When the interactions occur at a level of description other than that at which the patterns occur, these patterns are often called "emergent." Hence magnetism, which is a property of a large number of iron atoms (in this case with spins polarized in one direction) is an emergent property.
Economists have begun to use complex systems as the basis for formal modeling precisely in order to understand certain aggregate features of environments which are characterized by many heterogeneous actors. Examples are easy to construct. A stock market is comprised of many traders with idiosyncratic beliefs about the future. The actors exchange information and react to some types of common information, but stock prices are ultimately determined by a large number of decentralized buy and sell decisions. In a very different area, the composition of residential communities is determined by the individual preferences and location decisions of individual families.
Notice that in each case, there is a feedback between the aggregate characteristics of the economic environment under analysis and the individual actors which comprise that environment. Movements in stock prices influence the beliefs of individual traders and in turn influence their subsequent decisions. Families make community choices on the basis of expectations concerning community characteristics; these characteristics in turn evolve in response to individual choices. These feedbacks are in and of themselves not unique to complex environments. After all, elementary economics teaches us about the feedback between individual demand and supply decisions and market level prices.
Complexity deepens our understanding of economic phenomena by illustrating how various types of microeconomic structures lead to particular aggregate economic phenomena. For example, as illustrated in work by W. Brian Arthur, John Holland, Blake LeBaron, and Richard Palmer [1] booms and crashes in prices appears to be a frequent feature of stock market environments in which evolving rules-of-thumb behavior interact to determine individual purchasing decisions. Similarly, the emergence of racially segregated neighborhoods from a collection of individuals with different preferences for community racial composition has been illustrated by Thomas Schelling [2], in what is probably the first paper on economic complexity. Hence, the value of complex system thinking in the social sciences, for either researchers or policymakers, lies in its potential for enriching our understanding of the relationships between aggregate outcomes and individual decisions. While plausible theoretical complexity-based models have been developed to explain phenomena ranging from out of wedlock births (William Brock and Steven Durlauf) [3] to the distribution of city sizes (Paul Krugman) [4], these models have not been subjected to sufficient empirical scrutiny to assess their validity. In particular, the causal mechanisms which drive these models have yet to be shown to be empirically salient.
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