Learning and Tacit Collusion by Artificial Agents in Cournot Duopoly Games

We examine learning by artificial agents in repeated play of Cournot duopoly games. Our learning model is simple and cognitively realistic. The model departs from standard reinforcement learning models, as applied to agents in games, in that it credits the agent with a form of conceptual ascent, whereby the agent is able to learn from a consideration set of strategies spanning more than one period of play. The resulting behavior is markedly different from behavior predicted by classical economics for the single-shot (unrepeated) Cournot duopoly game. In repeated play under our learning regime, agents are able to arrive at a tacit form of collusion and set production levels near to those for a monopolist. We note that Cournot duopoly games are reasonable approximations for many real-world arrangements, including daily spot markets for electricity.