• Revealed Understanding
    • ABSTRACT: Many important choices require decision makers (DMs) to choose between alternatives that they do not fully understand. We model a DM making a choice under uncertainty who may imperfectly understand acts—mappings from states to outcomes. We model coarse understanding of acts using partitions of the state space: for each cell of the partition, the DM knows the set of outcomes that she could receive if the true state lies in that cell, but within each cell she is unable to match states with outcomes. A key feature of the model is that the DM may understand different acts using different partitions, depending on the acts' specific outcomes. We argue that this allows us to differentiate limited understanding of acts from coarse contingencies and ambiguity aversion, both related phenomena, using only static choice of acts. Our main results axiomatically characterize this model and uniquely identify the partitions that the DM uses to understand acts.

  • Bayesian Optimism, forthcoming Economic Theory
    • ŒABSTRACT: Theories of optimism typically hypothesize that optimism is driven by agents changing their beliefs, or view of the world. In this paper, we hypothesize that agents maintain their view of the world, but arrive at an optimistic belief by distorting the information used to update beliefs in a motivated way. We behaviorally identify the information used to update beliefs, which may be a distortion of the information the analyst observes. Given this identification, we provide a novel behavioral definition of optimism that alters Dynamic Consistency to account for both the distorted information and the optimistic nature of the distortion.