Agent-based computational economics (ACE) combines elements from economics and computer science. In this paper, we focus 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 we compare two important approaches that are dominating in ACE and show that the above practice may hinder the performance of the GA and thereby hinder agent learning. More specifically, we show 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. We also present improved social (ACE) simulation results for the Cournot oligopoly game, yielding (higher profit) Cournot-Nash equilibria instead of the competitive equilibria.