Dynamic learning in strategic games

Abstract Practical decision-making in business and government regularly involves interaction with competitors (and other agents such as suppliers and customers). These interactions can have a significant effect on optimal decision making. To determine optimal prices, for example, a monopoly firm may need to vary prices to learn consumer demand, but, with competition, varying prices may provide information to competitors that is counterproductive. This paper shows that, while firms in competition may be able to learn optimal best-response strategies over time, in some settings, this result is not an ex ante optimal strategy for the competitors when actions reveal information to others. In this situation, the firms can achieve a collaborative outcome in equilibrium by not fully exploring and learning the environment with both firms earning more than they would in a competitive equilibrium with full information. The result has implications for policies (such as price or production controls) that restrict firm actions or that require disclosures.