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Learning In Reverse Causal Strategic Environments With Ramifications on Two Sided Markets

2024/04/20 by Seamus Somerstep, Yuekai Sun, Somerstep, Seamus +3
Decision Sciences · Social Sciences · #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #Experimental Behavioral Economics Studies #FOS: Computer and information sciences #Game Theory and Applications #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.2404.13240

openalex publication_date 2024/04/20 · openalex created_date 2024/04/24 · openalex updated_date 2026/07/28

Abstract

Motivated by equilibrium models of labor markets, we develop a formulation of causal strategic classification in which strategic agents can directly manipulate their outcomes. As an application, we compare employers that anticipate the strategic response of a labor force with employers that do not. We show through a combination of theory and experiment that employers with performatively optimal hiring policies improve employer reward, labor force skill level, and in some cases labor force equity. On the other hand, we demonstrate that performative employers harm labor force utility and fail to prevent discrimination in other cases.

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