Department of Economics
University of Pennsylvania
Research Affiliate in Industrial Organization
Center for Economic Policy and Research Development (CEPR)
Phone: (908) 432-7889
I study various topics in microeconomics with a focus on models of information, including learning models where individuals have information-processing biases and how information influences equilibrium payoffs and behavior in dynamic games. I also work on questions related to discrimination, the optimal design of experiments and contracting, with applications to designing ratings systems and providing incentives in online labor markets.
Publications & Working Papers
- "Informational Herding with Model Misspecification." Journal of Economic Theory (2016).
- "Optimal Design of Experiments in the Presence of Interference," with S. Baird, C. McIntosh and B. Ozler. Accepted at Review of Economics & Statistics (2017).
Design software (cited below)
- "The Language of Discrimination: Using Experimental versus Observational Data," with A. Imas and M. Rosenberg. Forthcoming in American Economic Association (Papers & Proceedings) (2018).
- "Bounded Rationality and Learning: A Framework and a Robustness Result," with D. Hauser. R&R at Econometrica, (2017).
- "The Dynamics of Discrimination: Theory and Evidence," with A. Imas and M. Rosenberg. R&R at American Economic Review, (2017).
- "Using Persistence to Generate Incentives in a Dynamic Moral Hazard Problem." R&R at Theoretical Economics, (2016).
- "Optimal Contracting with Costly State Verification, with an Application to Crowdsourcing," with T. Kravitz, (2016).
- "Mediated Persistence," (2018). draft available upon request
- "Collective Search with Private Information," with S.N. Ali.
Work in Progress
- "Information Design in Misspecified Learning Models," with D. Hauser. (extended abstract)
- “An Expanded Framework of Discrimination in Economics” with Kareem Haggag, Alex Imas and Devin Pope
A. Bohren, P. Staples, S. Baird, C. McIntosh and B. Ozler, (2016). Power Calculation Software for Randomized Saturation Experiments, Version 1.0. Available from http://pdel.ucsd.edu/solutions/index.html
Environments: R, Python, Matlab, Graphical User Interface (GUI)