Learning and Self-confirming Long-Run Biases

We consider an uncertainty averse, sophisticated decision maker facing a recurrent decision problem where information is generated endogenously. In this context, we study self-confirming strategies as the outcomes of a process of active experimentation. We provide inter alia a learning foundation for self-confirming equilibrium with model uncertainty (Battigalli et al., 2015). We also argue that ambiguity aversion tends to stifle experimentation, increasing the likelihood that decision maker get stuck into suboptimal certainty traps.