Working Papers (My arXiv page)
Inertial Updating with General Information (with Adam Dominiak and Gerelt Tserenjigmid) - Revise and Resubmit at American Economic Review (slides)
idea: you are told that some outcome of interest will occur with some range of probabilities. how do you update your beliefs? the answer to this and a characterization of f-divergences inside.
This paper was previously titled Minimum Distance Updating With General Information.
Inertial Updating (with Adam Dominiak and Gerelt Tserenjigmid) - NEW!
idea: a framework for belief updating unifying Bayesian and non-Bayesian updating rules, along with rules for updating on null-events.
Misspecified Model Estimation and Its Impact on Predictions (with Junnan He, Lin Hu and Anqi Li) - Revise and Resubmit at American Economic Review
idea: how do you learn about media sources when you misperceive the bias in news reports? learn about that and more here. [arXiv]
Can an LLM Learn Preferences from Choice Data? (with Jeongbin Kim, Kyu-Min Lee, Euncheol Shin, and Hector Tzavellas) - Revised and Resubmitted to The Review of Economics and Statistics Slides
idea: some people
Conservative Updating - under revision
idea: some people do not change their beliefs enough when provided information. here's a preference based characterization of such behavior.
Selling to Wishful Thinkers (with Tommy Chan) - coming soon, preliminary draft available upon request.
idea: some people are overestimate the chances of good events. you can extract surplus from such wishful thinkers by introducing uncertainty.
The Focal Quantal Response Equilibrium (with Gerelt Tserenjigmid) - under review - NEW!
idea: some options draw more attention than others, even after controlling for their utilities. we incorporate this idea into a strategic setting and show accounting for focality explains some experimental findings.
Learning from an Unknown DGP: Experimental Evidence on Belief Updating with AI Recommendations (with Daniel Martin and Gerelt Tserenjigmid) - NEW!
idea: how do people learn from opaque AI recommendations? experimental evidence inside. [arXiv]