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 investigate experimentally the conjecture that loss avoidance solves the tension in stag-hunt games for which payoff dominance and risk dominance make conflicting predictions. Contrary to received textbook wisdom, money-losing outcomes do shift behavior, albeit not strongly, toward the...