On social learning and robust evolutionary algorithm design in economic games

Agent-based computational economics (ACE) combines elements from economics and computer science. In this paper, the authors focused on the relation between the evolutionary technique that is used and the economic problem that is modeled. In the field of ACE, economic simulations often derive parameter settings for the genetic algorithm directly from the values of the economic model parameters. In this paper, two important approaches that are dominating in ACE were compared and showed that the above practice may hinder the performance of the GA and thereby hinder agent learning. More specifically, it is shown that economic model parameters and evolutionary algorithm parameters should be treated separately by comparing the two widely used approaches to social learning with respect to their convergence properties and robustness. This leads to new considerations for the methodological aspects of evolutionary algorithm design within the field of ACE. Improved social (ACE) simulation results were also presented for the Cournot oligopoly game, yielding (higher profit) Cournot-Nash equilibria instead of the competitive equilibria.