학술논문

Learning In Reverse Causal Strategic Environments With Ramifications on Two Sided Markets
Document Type
Working Paper
Source
Subject
Statistics - Machine Learning
Computer Science - Computer Science and Game Theory
Computer Science - Machine Learning
Language
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.
Comment: 22 pages, 5 figures