Genetic algorithm learning and the cobweb model*

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.