Working papers
Categorization in Games: A Bias-Variance Perspective (with Jehiel, P.) Submitted
Idea: We present a framework for endogenous categorisation in games. On-path situations (nodes, information sets, or types) are distinguished perfectly, due to abundance of data, whereas off-path situations have to be categorised coarsely due to scarcity of data, bundling similar situations together. Application of the framework to classic examples yield novel predictions.
This work incorporates our previous working paper “Cycling and Categorical Learning in Decentralized Adverse Selection Economies”
Work in progress
"Preferences as Heuristics" (with A. Rigos)
“An Experiment on Observations on Cooperation” (with Heller, Y., and Embrey, M.)
"Comparing Models of Learning in Games" (with Afsar, A., Fudenberg, D., and Karreskog Rehbinder, G.)
"Normatively Relevant Narratives" (with Rigos, A., and Andersson, L.)
Retired working paper
“Asymptotically Optimal Regression Trees”
Idea: Optimal locally adaptive bin-size is derived for a version of regression trees. Consistency and asymptotic normality of the estimator is proved.