Etan Green
My current research applies deep reinforcement learning to real-world economic problems, such as bargaining and pricing. I am broadly interested in optimal behavior in complex decision-making environments, and how human behavior compares.
» etangr [at] upenn.edu
Bio
2021-present: Visiting Researcher, Arena-AI
2016-present: Assistant Professor of Operations, Information and Decisions, The Wharton School, University of Pennsylvania
2015-16: Postdoc Researcher, Microsoft Research, New York City
2015: Ph.D., Stanford University Graduate School of Business
2008-10: Associate Consultant, Bain & Company, San Francisco
2008: A.B., Brown University
Papers
The Science of the Deal: Optimal Bargaining on eBay Using Deep Reinforcement Learning,
Etan Green & Barry Plunkett.
Best Paper Award @ EC'22.
» An artificial intelligence that provides simple and effective strategies for bargaining in one of the world’s largest markets.
» A method for applying deep reinforcement learning to real-world economic problems.
paper |
talk |
github: rl |
github: data processing
The Favorite-Longshot Midas,
Etan Green, Haksoo Lee & David Rothschild.
Invited revision at Review of Economic Studies.
» Racetracks dupe bettors into overbetting longshots—and make more money as a result.
paper
Bayesian Instinct,
Etan Green & David Daniels.
Under review.
» Umpires are not biased—they're Bayesian.
paper |
talk |
github
A Sharp Test of the Portability of Expertise,
Etan Green, Justin Rao & David Rothschild.
Management Science, 2019.
» Expertise is not very portable.
paper |
supplement + data & code
Personal Bests as Reference Points,
Ashton Anderson & Etan Green.
Proceedings of the National Academy of Sciences, 2018.
» People care a lot about achieving their best-ever performance.
paper |
appendix |
data & code
What Does it Take to Call a Strike? Three Biases in Umpire Decision Making,
Etan Green & David Daniels.
MIT Sloan Sports Analytics Conference, 2014.
» Umpires are biased.
paper |
talk