| This dissertation is in Behavioral Decision Theory, an area of Economic Theory in which the aim is to extend the scope of decision-theoretic models to accommodate some of the existing behavioral and psychological evidence.;In the first chapter, I study a decision maker who gradually learns the utility of available alternatives. This learning process is interrupted when a choice must be made. From the observer's perspective, this formulation yields a random choice model. I propose and analyze one such learning process, the Bayesian Probit Process. I apply the model to the similarity puzzle of random choice. I provide a definition of similarity based only on the random choice process and show how the model (i) allows us to reconcile the similarity puzzle; and (ii) provides a rationale for related behavioral phenomena, such as the attraction and the compromise effects.;The second chapter is joint work with Faruk Gul and Wolfgang Pesendorfer. We study random choice to capture violations of the weak axiom of revealed preference. Using comparisons of choice probabilities, we introduce the notion of a stochastic preference. We show that the Luce model is the unique rule that has a context-independent stochastic preference. To address well-known difficulties of the Luce model in situations where choice objects have overlapping attributes, we introduce a new random choice model, the Weighted Attributes Rule. We show that it is identified by a context-independent stochastic preference over attributes.;The final chapter is a study of preference for flexibility in a context where the choice options are menus of alternatives. In this context, I offer a characterization of the property of ordinal submodularity and an interpretation of failures of this property as subjective pessimism. |
