In a model inspired by neuroscience, we show that constrained optimal perception encodes lottery rewards using an S-shaped encoding function and over-samples low-probability events. The implications of this perception strategy for behavior depend on the decision-maker’s understanding of the...

We study maximum-likelihood estimation and updating, subject to computational, cognitive, or behavioral constraints. We jointly characterize constrained estimates and updating within a framework reminiscent of a machine learning algorithm. Without frictions, the framework simplifies to standard...

We study a learning process in which subjects extrapolate from their experience of similar past strategic situations to the current decision problem. When applied to coordination games, this learning process leads to contagion of behavior from problems with extreme payoffs and unique equilibria...