Using genetic algorithms to model the evolution of heterogeneous beliefs

James Bullard and John Duffy

No 1994-028, Working Papers from Federal Reserve Bank of St. Louis

Abstract: Genetic algorithms have been used by economists to model the process by which a population of heterogeneous agents learn how to optimize a given objective. However, most general equilibrium models in use today presume that agents already know how to optimize. If agents face any uncertainty, it is typically with regard to their expectations about the future. In this paper, we show how a genetic algorithm can be used to model the process by which a population of agents with heterogeneous beliefs learns how to form rational expectation forecasts. We retain the assumption that agents optimally solve their maximization problem at each date given their beliefs at each date. Agents initially lack the ability to form rational expectations forecasts and have, instead, heterogeneous beliefs about the future. Using a genetic algorithm to model the evolution of these beliefs, we find that our population of artificial adaptive agents eventually coordinates their beliefs so as to achieve a rational expectations equilibrium of the model. We also report the results of a number of computational experiments that were performed using our genetic algorithm model.

Keywords: Econometrics; time series analysis (search for similar items in EconPapers)
Date: 1994
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Citations: View citations in EconPapers (1)

Published in Computational Economics, February 1999, 13(1), pp. 41-60

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Related works:
Working Paper: Using genetic algorithms to model the evolution of heterogenous beliefs (2010)
Journal Article: Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs (1999)
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DOI: 10.20955/wp.1994.028

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