Publications
Belief in Mean Reversion and the Law of Small Numbers (joint with Jawwad Noor) forthcoming Theoretical Economics
This paper was previously circulated as "An Axiomatic Approach to the Law of Small Numbers"
Mixture-betweenness: Uncertainty and Commitment Journal of Economic Theory, Volume 230, December 2025
This paper supersedes “Mixture-Dependent Preference for Commitment”, which develops a version of Theorem 3.1 for Associative Mixture Spaces. The earlier paper is available here
Revealed Preference and the Subjective State Space Hypothesis , Journal of Mathematical Economics Volume 82, May 2019, Pages 61-68
Similarity-based mistakes in choice, with L. Ülkü, Journal of Mathematical Economics Volume 61, December 2015, Pages 152-156
Other Publications
Implications of the Patient Protection and Affordable Care Act on Insurance Coverage and Rehabilitation Use Among Young Adult Trauma Patients with Cheryl Zogg, John Scott, Lindsey Wolf , Thomas Tsai , Peter Najjar, Olubode Olufajo, Eric Schneider, Elliott Haut, Adil Haider, Joseph Canner, JAMA surgery Volume 151, December 2016
Componentes Principales: Las Dimensiones de la Confianza with Kin Gutierrez Olivares y Max Mergenthaler, Laberintos y Infinitos No 39, Pg 35-41 2015
Working Papers
Updating under Imprecise Information (joint with Yi-Hsuan Lin) revise and resubmit at JET
This paper models an agent that ranks actions with uncertain payoffs after observing a signal that could have been generated by multiple objective information structures. Under the assumption that the agent's preferences conform to the multiple priors model (Gilboa and Schmeidler (1989)) ), we show that a simple behavioral axiom characterizes a generalization of Bayesian Updating. Our axiom requires that whenever all possible sources of information agree that it is more 'likely' for an action with uncertain payoffs to be better than one with certain payoffs, the agent prefers the former. We also provide axiomatizations for several special cases. Finally, we consider the situation where the informational content of a signal is purely subjective. We characterize the existence of a subjective set of information structures under full Bayesian updating for two extreme cases: (i) No ex-ante state ambiguity, and (ii) No signal ambiguity.
Modeling the Modeler (joint with Evan Piermont) (Supplemental Appendix) submitted
We study a modeler whose goal is to (partially) identify a subject’s type (preferences) through revealed preference analysis. We propose three normative principles for how the modeler should rank choice experiments: Structural Invariance, Identification Separability, and Information Monotonicity. Our main result shows that these principles characterize preferences consistent with maximizing Expected Identification Value, a functional form that unifies several theories of experimental design. A notable special case occurs when the value of identification depends on beliefs given a potential identification, allowing the modeler to view experiments as lotteries. This risk-analogy yields a tractable and intuitive specification.
A Cognitive Theory of Ambiguity Attitudes (joint with Norio Takeoka and Jianming Xia) (supplementary material) submitted
This paper provides axiomatic foundations for a model in which ambiguity attitudes are endogenously determined through cognitive optimization. The decision maker evaluates acts using alternative non-additive aggregation rules and optimally trades off the benefits of less ambiguity-averse evaluation against cognitive cost. The resulting framework generalizes Choquet expected utility and accounts for preference reversals identified by Machina (2009). The model is characterized by a novel axiom, Comonotonic Convexity, which regulates the evaluation of mixtures by requiring the decision maker to avoid hedging whenever it yields no benefit. We interpret this axiom as reflecting aversion to unnecessary complexity in the evaluation of acts.
A Subjective Theory of Belief in the Gambler’s Fallacy (joint with Jawwad Noor) submitted
Studies suggest that people often believe that random processes exhibit reversion to the mean, regardless of the size of the sample. In a canonical coin-tossing environment, a belief in mean reversion towards some bias is straightforward to define if it is assumed that the tosses are objectively i.i.d. with the same bias . In this paper we define and represent a belief in mean reversion without assuming the existence of an objective random process. This decouples the notion of a belief in mean reversion from the notion of misspecified beliefs that is predominantly used in the literature.
Deception under the Veil of Noise (joint with Jawwad Noor)
We study a dynamic predator–prey game in which a predator can conceal its movement under naturally occurring environmental noise. In the safe state, forest noise is i.i.d., whereas in the dangerous state the predator contributes additional noise as it approaches the prey. The prey updates her beliefs about danger from the realized noise sequence and chooses whether to remain vigilant. We characterize equilibrium patterns of noise generated in the forest and show that a marker for deception is a hot-hand effect, whereby streaks persist with increasing probability
Alpha-MaxMin Updating Rules for Ambiguous Information (joint with Yi-Hsuan Lin)
This paper develops two belief-updating rules for ambiguous information structures, which map each state of the world to a distribution over sets of signal realizations. Grounded in first principles, our updating rules are derived through contingent planning reasoning that adheres to dynamic consistency. Two distinct equations characterize the resulting families of rules, depending on how a decision maker conceives the order of states and signals. Each rule is parameterized by a single index capturing pessimism or optimism toward ambiguity, and both allow for the possibility that news may be all good or all bad. The rules diverge, however, in two key dimensions: whether state-independent information structures preserve the prior, and whether mixing two information structures yields an intermediate posterior.
Work in Progress
Bayesian Adaptive Choice Experiments (joint with Marshall Drake, Neil Thakral, and Linh Tô)
Package
On the Limits of Revealed Preferences
Ambiguous Subjective States (joint with Norio Takeoka)
Anxiety, Worry, and Dynamic Choice under Uncertain Threat (joint with Agustin Troccoli )