I am a Ph.D. candidate in Economics at the University of California, Irvine (UCI). I am on the 2025-26 job market. I specialize in decision theory, experimental economics, and behavioral economics. My current research focuses on devising efficient algorithms to fit utility functions to choice data from the real world and experiments in the lab.

with Igor Kopylov

Abstract: We model stochastic choice rules  via  finitely many types θ that maximize distinct expected utility functions  and use  endogenous tie-breaking rules. First, we characterize discrete random expected utility (DREU) where the likelihood μ(θ) of each relevant type θ is preserved across all menus A. This model is a discrete version for the random expected utility of  Gul and Pesendorfer (2006), but our axioms, identification, and tie-breaking procedures are novel. More generally, we propose discrete-map expected utility (DMEU) where the likelihoods μA(θ) can vary continuously with the menu A. The map μ is identified uniquely in our model. DMEU captures various kinds of context dependence with both behavioral and normative motivations. Our main application of DMEU is a model of self-selection, where types can increase their participation rates across distinct menus when their best choices are improved. The standard monotonicity principle for stochastic choices delivers a self-selective property for the map μ. We argue that self-selection in menus of lotteries can be consistent with the Block--Marschak random utility model over menus of deterministic outcomes, but the identification of types in the latter approach becomes misspecified. Finally, we discuss  applications of DREU and DMEU to random risk aversion  and random Cobb-Douglas utility.