Reputation markets

Abstract

A reputation system should incentivize users to obtain and reveal estimates of content quality. It should also aggregate these estimates to establish content reputation in a way that counters strategic manipulation. Mechanisms have been proposed in recent literature that offer financial incentives to induce these desirable outcomes. In this paper, to systematically study what we believe to be fundamental characteristics of these mechanisms, we view them as information markets designed to assess content quality, and refer to them as reputation markets. Specifically, we develop a rational expectations equilibrium model to study how incentives created by reputation markets should influence community behavior and the accuracy of assessments. Our analysis suggests that reputation markets offer a number of desirable features: - As the quality of information improves or the cost of information acquisition decreases, reputation assessments become increasingly robust to manipulation. - If users can pay to acquire information, errors in reputation assessments do not depend on uncertainty in the manipulator's intent. - Reputation distortion incurs cost to the manipulator, resulting in cash transfers to other users. - Pseudonyms do not help a manipulator distort reputations.