Research Interests

Economic Theory, Decision Theory, Behavioral Economics


Random Choice as Behavioral Optimization
with Faruk Gul and Wolfgang Pesendorfer, Econometrica, Vol. 82, No. 5 (September, 2014), 1873–1912.

We develop an extension of Luce’s random choice model to study violations of the weak axiom of revealed preference. We introduce the notion of a stochastic preference and show that it implies the Luce model. Then, to addresses well-known difficulties of the Luce model, we define the attribute rule and establish that the existence of a well-defined stochastic preference over attributes characterizes it. We prove that the set of attribute rules and random utility maximizers are essentially the same. Finally, we show that both the Luce and attribute rules have a unique consistent extension to dynamic problems.

Random Choice and Learning
Updated October 2017. Journal of Political Economy, forthcoming.

Context-dependent individual choice challenges the principle of utility maximization. We explain context-dependence as the optimal response of an imperfectly informed agent to the ease of comparison of the options. We introduce a discrete-choice model, the Bayesian probit, which allows the analyst to identify stable preferences from context-dependent choice data. Our model accommodates observed behavioral phenomena —including the attraction and compromise effects— that lie beyond the scope of any random utility model. We use data from frog mating choices (Lea and Ryan, 2015) to illustrate how our model can outperform the random utility framework in goodness of fit and out-of-sample prediction.

Subjective Ambiguity and Preference for Flexibility 
with Leandro Gorno, Journal of Economic Behavior & Organization, Vol. 154, (October, 2018), 24–32.

A preference over menus is monotonic when every menu is at least as good as any of its subsets. We show that every utility representation for a monotonic preference is equal to the minmax value of a decision maker whose payoff depends on the option chosen from the menu and on the realization of a subjective state. This representation suggests a decision maker who faces uncertainty about her own future tastes and who exhibits an extreme form of pessimism with respect to this uncertainty. In the case of finitely many alternatives, we provide a characterization of monotonic preferences which relaxes the submodularity axiom of Kreps (1979). We characterize the minimal state space needed for our representation, and we show that the second period choice behavior of our decision maker differs from the one implied by the costly contemplation model of Ergin (2003).

Random Evolving Lotteries and Intrinsic Preference for information
with Faruk Gul and Wolfgang Pesendorfer. Updated March 2018. Revise and Resubmit, Econometrica.

We introduce random evolving lotteries to study preference for non-instrumental information and history-dependent attitudes to risk-consumption. We provide a representation theorem for  non-separable risk-consumption preferences and analyze the trade off between smooth consumption paths and hedging path risk. We characterize information seeking and its opposite, information aversion. We show how our rich set of choice objects allows nuanced attitudes to information, including a preference for savoring the prospect of positive surprises, and the dreading of news that will arrive soon.


Preference Reversal or Limited Sampling? Maybe túngara frogs are rational after all.
(This working paper is superseded by Random Choice and Learning, forthcoming, see above)
[Paper][Slides] November 2016

[Podcast] interview by Claire Gauen in Hold That Thought

[Non-technical summary] Lea and Ryan (Science, Reports, 28 August 2015, p. 964) interpret mate choice data collected from frogs in the laboratory as being incompatible with rational choice models currently used in sexual selection theory. A close look at their data supports the hypothesis that some options offered in the lab are easier to compare than others. If we take into account that some pairs of options are easier to compare, and that frogs operate under conditions of uncertainty, we can restore rationality to túngara frogs.


Moderate Expected Utility
with Junnan HeWorking paper, updated March 2019.

Individual choice data often violates strong stochastic transitivity (SST) while conforming to moderate stochastic transitivity (MST). We propose a slightly stronger version of the MST postulate, which we call MST+, and we show that MST and MST+ retain significantly more predictive power than weak stochastic transitivity (WST). Our first theorem shows that a binary choice rule satisfies MST+ if and only if it can be represented by a moderate utility model with two parameters: a utility function describes the value of each option, and a distance metric determines their the degree of comparability. Our second theorem introduces the moderate expected utility model and shows how utility and distance can be identified from choice data over lotteries.


Optimal Decoys
with Carl Sanders. PDF coming soon.

A rational decision maker with imperfect information about the value of options takes their varying degrees of comparability into account in order to maximize the expected value of her choice. From the point of view of the analyst, the resulting choice behavior is context-dependent and incompatible with standard discrete choice estimation tools. We use the Bayesian probit model to measure preferences, information precision, and comparability using the context-dependent choice data from Soltani, De Martino and Camerer (2012). We provide novel comparative statics for discrete choice analysis in multi-attribute settings by relating these measurements to the observable characteristics of the options. We analyze the data for 21 subjects at the aggregate and individual levels, and provide novel welfare statements that incorporate the decision maker’s operational risk at the individual level.

Work in Progress

Closed-form Choice Probabilities in the Multinomial Probit Model

Sour Grapes and Optimal Expectations [Slides]

Curriculum Vitae
[Download PDF]

Mailing Address
Campus Box 1208
1 Brookings Drive
St. Louis, MO 63130