1) Global coordination under Monotone Sampling (SSRN, download). Joint with Srinivas Arigapudi.
Idea: The paper unifies many models of learning and shows that when new agents learn from independent samples using a shared monotone rule, coordination always wins.
2) Feasible Diversity: Individually Fair Lotteries with Intersectional Constraints (SSRN, download). Joint with Ron Peretz and Amnon Schreiber.
Idea: Two diversity dimensions always allow feasible proportional, individually fair selection—three or more may not, but efficient algorithms still achieve near-proportional fairness with only tiny violations
3) An evolutionary approach to multi-dimensional learning with application to firms (download, presentation: pptx, pdf). Joint with Srinivas Arigapudi, Omer Edhan, Ziv Hellman. Revision requested at Theoretical Economics.
Idea: We study learning dynamics in which new agents learn how to play by combining traits from various successful incumbents.
4) Strategies in the repeated prisoner's dilemma: A cluster analysis (download, GitHub data and code). Joint with Itay Tubul. Revision requested at Games & Economic Beahvior.
Idea: We apply a new technique of cluster analysis to study which strategies are used in experiments of the infinitely repeated prisoner's dilemma.
5) Miscoordination under sample-based learning (SSRN, download). Joint with Srinivas Arigapudi.
Idea: Small-sample learning can trap groups in mismatch, especially when experience levels differ or people care a lot about what others do.
6) The Hhdden advantage of loss: Evidence from chess (SSRN, download). Joint with Itay Tubul.
Idea: Chess players play better after losing, suggesting setbacks can sharpen performance.
7) Uniqueness of inflection points in binomial exceedance function compositions (GDrive download). Joint with Srinivas Arigapudi and Amnon Schreiber. Revision requested at Examples and Counterexamples.
Idea: We show that compositions of binomial exceedance functions have unique inflection points, and discuss the interesting implications of this property.