Subsidizing Lemons for Efficient information aggregation

Abstract

We study how social planning can reduce the inefficiencies of social learning, stemming from herding and informational cascades. A social planner is introduced to the classical sequential social learning model. She can tax or subsidize players’ actions in order to maximize social welfare, a discounted sum of agents’ utilities. We solve or accurately approximate the expected utility of the social planner and the optimal pricing strategy for various signal distributions. In equilibrium, it is optimal to increase the price for the better action, causing a reduction in current agent’s utility, but also a net gain, due to the information this action reveals. The addition of the social planner significantly improves social welfare and the asymptotic speed of learning.

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Vadim Martynov
Ph.D. student in Quantitative Economics

My research interests include distributed robotics, mobile computing and programmable matter.