-learning agents in a Cournot oligopoly model

Abstract

QQ-learning is a reinforcement learning model from the field of artificial intelligence. We study the use of QQ-learning for modeling the learning behavior of firms in repeated Cournot oligopoly games. Based on computer simulations, we show that QQ-learning firms generally learn to collude with each other, although full collusion usually does not emerge. We also present some analytical results. These results provide insight into the underlying mechanism that causes collusive behavior to emerge. QQ-learning is one of the few learning models available that can explain the emergence of collusive behavior in settings in which there is no punishment mechanism and no possibility for explicit communication between firms.