Assistant Professor of Economics
Random Choice and Learning.
Journal of Political Economy, Vol. 127, No. 1, (February, 2019), 419–457.
Subjective Ambiguity and Preference for Flexibility, with Leandro Gorno.
Journal of Economic Behavior & Organization, Vol. 154, (October, 2018), 24–32.
The Thrill of Gradual Learning
We report on an experiment that shows subjects prefer a gradual resolution of uncertainty when information about winning yields decisive bad news but inconclusive good news. This behavior is difficult to reconcile with existing theories of choice under uncertainty, including the Kreps-Porteus model. We show how the behavioral patterns uncovered by our experiment can be understood as arising from subjects’ special emphasis on their best (peak) and worst (trough) experiences along the realized path of uncertainty.
- Working paper (PDF) October 2020.
Random Evolving Lotteries and Intrinsic Preference for Information
We introduce random evolving lotteries to study preference for non-instrumental information. Each period, the agent enjoys a flow payoff from holding a lottery that will resolve at the terminal date. We provide a representation theorem for non-separable risk-consumption preferences and use it to characterize information seeking and its opposite, information aversion. To address applications, we characterize peak-trough utilities that aggregate trajectories of flow utilities linearly but, in addition, put weight on the best (peak) and worst (trough) lotteries along each path. We identify conditions for the ostrich effect, decision makers’ tendency to prefer information after good news to information after bad news. Our model permits savoring (enjoying the gradual arrival of good and sudden arrival of bad news) and dread (disliking the gradual arrival of bad and sudden arrival of good news) and a preference for skewed information.
- Working paper (PDF) updated October 2019.
Moderate Expected Utility
With Junnan He.
Accounting for product differentiation is a key concern in the analysis of consumer demand: any consumer who abandons their first choice after a price increase is more likely to substitute it with options that are “close” in characteristics to the original choice than with more “distant” options. We provide a behavioral foundation for using a distance metric to measure product differentiation in discrete choice. This approach is captured by a simple formula in the moderate utility model. We identify a single, directly testable property that characterizes the model: choices are moderately transitive. We show that the model allows the analyst to accommodate well-known failures of strong transitivity, while retaining significantly more empirical bite than weak transitivity, achieving a useful compromise. Extending the analysis to the domain of risky choice, we introduce and characterize the moderate expected utility model, and we show how the analyst can measure both differentiation and utility from observed choice behavior.
Preference Reversal or Limited Sampling? Maybe túngara frogs are rational after all.
Work in Progress
With Carl Sanders.
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.
- Slides (PDF) updated July 2018.