On the computation of equilibria in discounted stochastic dynamic games

Abstract

We consider an ambiguity averse, sophisticated decision maker facing a recurrent decision problem where information is generated endogenously. In this context, we study self-confirming actions as the outcome of a process of active experimentation. We provide inter alia a learning foundation for self-confirming equilibrium with model uncertainty (Battigalli et al., 2015), and we analyze the impact of changes in ambiguity attitudes on convergence to self-confirming equilibria. We identify conditions under which the set of self-confirming equilibrium actions is invariant to changes in ambiguity attitudes, and yet ambiguity aversion may affect the dynamics. Indeed, we argue that ambiguity aversion tends to stifle experimentation, increasing the likelihood that the decision maker gets stuck into suboptimal “certainty traps.”