Using genetic algorithms to model the evolution of heterogeneous beliefs
AbstractGenetic 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 1994-028.
Date of creation: 1994
Date of revision:
Publication status: Published in Computational Economics, February 1999, 13(1), pp. 41-60
Other versions of this item:
- Bullard, James & Duffy, John, 1999. "Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs," Computational Economics, Society for Computational Economics, vol. 13(1), pages 41-60, February.
- James Bullard & John Duffy, 2010. "Using genetic algorithms to model the evolution of heterogenous beliefs," Levine's Working Paper Archive 550, David K. Levine.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Arifovic, Jasmina, 1995. "Genetic algorithms and inflationary economies," Journal of Monetary Economics, Elsevier, vol. 36(1), pages 219-243, August.
- Routledge, Bryan R, 1999. "Adaptive Learning in Financial Markets," Review of Financial Studies, Society for Financial Studies, vol. 12(5), pages 1165-1202.
- Arifovic, Jasmina & Eaton, Curtis, 1995.
"Coordination via Genetic Learning,"
Society for Computational Economics, vol. 8(3), pages 181-203, August.
- Bullard James, 1994.
Journal of Economic Theory,
Elsevier, vol. 64(2), pages 468-485, December.
- Arifovic, Jasmina, 1996. "The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental Economies," Journal of Political Economy, University of Chicago Press, vol. 104(3), pages 510-41, June.
- Thomas J. Sargent & Neil Wallace, 1981. "Some unpleasant monetarist arithmetic," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall.
- Marimon, Ramon & Sunder, Shyam, 1994.
"Expectations and Learning under Alternative Monetary Regimes: An Experimental Approach,"
Springer, vol. 4(1), pages 131-62, January.
- Ramon Marimon & Shyam Sunder, 1993. "Expectations and learning under alternative monetary regimes: An experimental approach," Economics Working Papers 37, Department of Economics and Business, Universitat Pompeu Fabra.
- Marimon, R. & Sunder, S., 1993. "Expectations and Learning under Alternative Monetary Regimes: An Experimental Approach," Papers 189, Cambridge - Risk, Information & Quantity Signals.
- Bullard, James & Duffy, John, 1998.
"A model of learning and emulation with artificial adaptive agents,"
Journal of Economic Dynamics and Control,
Elsevier, vol. 22(2), pages 179-207, February.
- James Bullard & John Duffy, 1994. "A model of learning and emulation with artificial adaptive agents," Working Papers 1994-014, Federal Reserve Bank of St. Louis.
- Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
- Arifovic, Jasmina & Bullard, James & Duffy, John, 1997. " The Transition from Stagnation to Growth: An Adaptive Learning Approach," Journal of Economic Growth, Springer, vol. 2(2), pages 185-209, July.
- repec:fth:simfra:94-11 is not listed on IDEAS
- James Bullard & John Duffy, 1995. "On learning and the stability of cycles," Working Papers 1995-006, Federal Reserve Bank of St. Louis.
This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page. reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Anna Xiao).
If references are entirely missing, you can add them using this form.