AI

A Game-Theoretic Analysis of Rank-Order Mechanisms for User-Generated Content

Abstract

We investigate the widely-used rank-order mechanism for displaying user-generated content, where contributions are displayed on a webpage in decreasing order of their ratings, in a game-theoretic model where strategic contributors benefit from attention and have a cost to quality. We show that the lowest quality elicited by this rank-order mechanism in any mixed-strategy equilibrium becomes optimal as the available attention diverges. Additionally, these equilibrium qualities are higher, with probability tending to 1 in the limit of diverging attention, than those elicited by a more equitable proportional mechanism which distributes attention in proportion to the positive ratings a contribution receives, but the proportional mechanism elicits a greater number of contributions than the rank-order mechanism.