A Model of Joint Learning in Poverty: Coordination and Recommendation Systems in Low-Income Communities

Abstract

We study a game-theoretic model of how individuals learn by observing others' acting, and how (causal) knowledge grows in communities as result. We devise a cooperative solution in this game, which motivates a new recommendation system where causality (not correlation) is the central concept. We use the system in low-income communities, where individuals make judgments about the efficiency of educational activities ("if I take course x, I will get a job"). We show that, uncoordinated, individuals easily "herd" on visible but ineffectual actions. And, in turn, that, coordinated, individuals become massively more responsive - with the intelligence to quickly discern errors, mark them, share them, and move there from, towards "what really works."