Can They Beat the Cournot Equilibrium? Learning with Memory and Convergence to Equilibria in a Cournot Oligopoly

This article analyses the ability of the learning firms in a Cournot oligopoly to discover market solutions more collusive that the Cournot equilibrium (CE). We start from the results of Vallée and Yildizoglu (2009) and of Alos-Ferrer (2004), and qualify the role of random experimenting, social learning (imitation), and (updated) memory in helping firms to discover such solutions. Our new computational results show, in contradiction with Alos-Ferrer (2004), that memory and its continuous update can indeed allow firms to beat the CE, and benefit from significant periods with higher profits. We show that the results of the literature on evolutionary learning in oligopoly can analytically be characterized through the interaction of three forces, and indicate when these forces can yield more collusive outcomes. We confirm these results with complementary computational experiments that clearly show the role of long memory