Working Papers
Nudge Me: Preferences over Default-Setting Rules (with David J. Freeman and Hanh Tong)
This paper studies how choices are influenced by the procedure used to select the default option. We develop an approach to test and compare default bias across different default-setting rules while controlling for heterogeneous preferences. We apply it to a within-subjects experimental design lottery choice experiment to compare four different default-setting rules: Random defaults, Custom defaults selected based on an individual’s own past choices, Social defaults selected based on others’ choices, and Expert-set defaults. We find that the content of default-setting rules matters: default bias is present for all non-random default-setting rules we study, but not for randomly set defaults.
Previously circulated under the title: Default-Setting and Default Bias: Does the Choice Architect Matter?
Difficult Decisions (with Yoram Halevy and David Walker-Jones)
We investigate the problem of identifying incomplete preferences in the domain of uncertainty by proposing an incentive-compatible mechanism that bounds the behavior that can be rationalized by very general classes of complete preferences. Hence, choices that do not abide by the bounds indicate that the decision maker cannot rank the alternatives. Data collected from an experiment that implements the proposed mechanism indicates that when choices cannot be rationalized by Subjective Expected Utility they are usually incompatible with general models of complete preferences. Moreover, behavior that is indicative of incomplete preferences is empirically associated with deliberate randomization.
Published Papers
(Non-) Parametric Recoverability of Preferences and Choice Prediction
Review of Economics and Statistics (2024), 106(1): 217-229
Simple functional forms for utility require restrictive structural assumptions which are often contrary to observed behavior. Even so, they are used broadly in applied economic research. I address this issue using a two-part, adaptive experimental design to compare the predictions of a popular, parametric model of decision making under risk to those of non-parametric bounds on indifference curves. Interpreting the latter as an approximate upper bound, I find the parametric model sacrifices very little in terms of predictive success. This suggests that, despite their restrictiveness, simple functional forms may nevertheless be useful representations of preferences over risky alternatives.
Previous versions circulated under the (vastly inferior) titles: Revealed-Preference-Based Methods for Recovering Preferences: An Experimental Comparison; Quantifying the Trade-off between Identification and Misspecification: Application to Choice under Risk
Parametric Recoverability of Preferences (with Yoram Halevy and Dotan Persitz)
Journal of Political Economy (2018), 126(4), 1559-1593
Revealed preference theory is brought to bear on the problem of recovering approximate parametric preferences from consistent and inconsistent consumer choices. We propose measures of the incompatibility between the revealed preference ranking implied by choices and the ranking induced by the considered parametric preferences. These incompatibility measures are proven to characterize well-known inconsistency indices. We advocate a recovery approach that is based on such incompatibility measures, and demonstrate its applicability for misspecification measurement and model selection. Using an innovative experimental design we empirically substantiate that the proposed revealed-preference-based method predicts choices significantly better than a standard distance-based method. [Link to version on publisher's website]
Non-Parametric Bounds for Non-Convex Preferences (with Yoram Halevy and Dotan Persitz)
Journal of Economic Behavior and Organization (2017), 137, 105-112
Given a data set of choices from linear budget sets, Varian (1982) uses revealed preference theory to construct bounds on the indifference curve that go through a given bundle. We claim that these bounds do not apply for non-convex preferences, and therefore may lead to erroneous welfare analysis. [Link to version on publisher's website]
Works in Progress
Computational Difficulty and Stochastic Choice: An Experiment (with Yoram Halevy)