Search and Satisficing PDF
American Economic Review, 2011
We develop a search-theoretic choice experiment to study incomplete consideration and choice quality.
Research
Publications
Published papers, with links to papers, appendices, data, and replication resources where available. Paper title links open PDF files.
A Testable Theory of Imperfect Perception PDF
Economic Journal, 2015
We introduce state-dependent stochastic choice data and NIAS, an axiom characterizing choice by an agent with private information.
Measuring Rationality with the Minimum Cost of Revealed Preference Violations PDF
Review of Economics and Statistics, 2016
We offer a measure of how close a set of choices is to satisfying preference maximization and apply it to household consumption data.
The Dual-Process Drift Diffusion Model: Evidence from Response Times PDF
Economic Inquiry, 2016
We propose a response-time and choice model that combines dual-process models with the drift diffusion model.
Strategic Pricing with Rational Inattention to Quality PDF
Games and Economic Behavior, 2017
I study how sellers set prices when facing buyers who are rationally inattentive to product quality.
Defaults and Attention: The Drop Out Effect PDF
Revue Économique, 2017
We measure the drop out effect, where a default option is accepted with little regard for personal suitability.
What Do Consumers Learn from Regulator Ratings? Evidence from Restaurant Hygiene Quality Disclosures PDF
Journal of Economic Behavior & Organization, 2021
We provide evidence on how individuals extract information from regulator ratings.
Is No News (Perceived As) Bad News? An Experimental Investigation of Information Disclosure PDF
American Economic Journal: Microeconomics, 2021. AEJ: Micro Best Paper Award.
We use laboratory experiments to directly test forces that drive disclosure theory.
Predictive Power in Behavioral Welfare Economics PDF
Journal of the European Economic Association, 2021
We empirically evaluate the predictive power of two behavioral welfare relations.
Comparison of Decisions under Unknown Experiments PDF
Journal of Political Economy, 2021
We identify when every experiment consistent with one set of decisions has a higher value of information than every experiment consistent with another set.
Complex Disclosure PDF
Management Science, 2022. Featured Article.
We implement a mandatory disclosure game where senders truthfully report private information but choose report complexity.
A Robust Test of Prejudice for Discrimination Experiments PDF
Management Science, 2022. Fast Track.
We show when average outcomes in a discrimination experiment provide evidence of prejudice, regardless of learning about individuals.
Cognitive Costs and Misperceived Incentives: Evidence from the BDM Mechanism PDF
European Economic Review, 2022
We model agents as paying cognitive costs to better understand BDM incentives and find experimental evidence consistent with the model.
Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals PDF
PLoS ONE, 2024
We study emerging norms around AI use in manuscript preparation and how those norms might be enforced.
Rational Inattention in Games: Experimental Evidence PDF
Experimental Economics, 2024
We experimentally implement a buyer-seller game to test whether attention responds rationally to strategic incentives.
Rationalizable Learning PDF
Economic Theory, 2024
We study what an analyst can infer from choice data about what a decision maker has learned.
Modeling Machine Learning: A Cognitive Economic Approach PDF
Journal of Economic Theory, 2025
We investigate whether modern machine learning predictions are consistent with economic models of human cognition.
The ABC's of Who Benefits from Working with AI: Ability, Beliefs, and Calibration PDF
Management Science, 2025
We use a controlled experiment to show that ability and belief calibration jointly determine the benefits of working with AI.
Harnessing Human Uncertainty to Train More Accurate and Aligned AI Systems PDF
Decision Analysis, 2026
We propose an AI-augmented decision-making approach that uses human labeler uncertainty to improve accuracy and alignment.