Genetic algorithm learning and the cobweb model

Journal of Economic Dynamics and Control 18 (1994) 3-28. North-Holland Genetic algorithm learning and the cobweb model* Jasmina Arifovic McGill University, Montrkal, Qub. H3A 2T7, Canada Received March 1992, final version received December 1992 This paper presents the cobweb model in which competitive firms, in a market for a single good, use a genetic algorithm to update their decision rules about next-period production and sales. The results of simulations show that the genetic algorithm converges to the rational expectations equilibrium

for a wider range of parameter values than other algorithms frequently studied within the context of the cobweb model. Price and quantity patterns generated by the genetic algorithm are also compared to the data of experimental cobweb economies. It is shown that the algorithm can capture several features of the experimental behavior of human subjects better than three other learning algorithms that are considered. 1. Introduction Departure from the hypothesis that economic agents form rational expecta- tions implies that a specific learning algorithm has to be employed in order to describe the way in which agents make decisions about their economic behavior. On the other hand, application of a particular algorithm faces a criticism of the arbitrariness of choice. Lucas (1986) suggests that comparison of the behavior of learning algorithms with the behavior exhibited in experimental economies with human subjects may be a possible way to address this problem. Thus, if learning algorithms, when applied to the same economic environ- ment, result in different behavior, observations from laboratory experiments with human subjects may be used to determine which algorithm is more successful in describing actual human behavior. In this paper, a genetic algo- rithm (GA), developed by Holland (1970a), is used to model learning of Correspondence to." Jasmina Arifovic, Department of Economics, McGill University, 855 Sher- brooke Street W., Montr6al, Qu6. H3A 2T7, Canada. *This paper derives from my doctoral dissertation. My special thanks go to my advisors Robert Lucas, Thomas Sargent, and Michael Woodford for their valuable help and ideas. Helpful sugges- tions were also received on an earlier draft from three anonymous referees. 0165-1889/94/$06.00 © 1994--Elsevier Science Publishers B.V. All rights reserved 4 J. Arifovic, Genetic algorithm learning and the cobweb model economic agents in the cobweb model. The results obtained with the application of the GA are compared to the behavior observed in cobweb experiments with human subjects and to the results obtained in studies of other learning algo- rithms within the context of the same model. The objective is to examine if the GA can account for some of the results of the experimental economies which differ from the predictions of other adaptive schemes. The cobweb model is a model of a market for a single good in which firms that are price takers make their production decision in every time period before they observe a market price. Total quantity supplied and the exogenously given demand...

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