Socially-Optimal Design of Service Exchange Platforms with Imperfect Monitoring
Yuanzhang Xiao, Mihaela van der Schaar · ACM Transactions on Economics and Computation · 2015
We study the design of service exchange platforms in which long-lived anonymous users exchange services with each other. The users are randomly and repeatedly matched into pairs of clients and servers, and each server can choose to provide high-quality or low-quality services to the client with whom it is matched. Since the users are anonymous and incur high costs (e.g., exert high effort) in providing high-quality services, it is crucial that the platform incentivizes users to provide high-quality services. Rating mechanisms have been shown to work effectively as incentive schemes in such platforms. A rating mechanism labels each user by a rating, which summarizes the user's past behaviors, recommends a desirable behavior to each server (e.g., provide higher-quality services for clients with higher ratings), and updates each server's rating based on the recommendation and its client's report on the service quality. Based on this recommendation, a low-rating user is less likely to obtain high-quality services, thereby providing users with incentives to obtain high ratings by providing high-quality services. However, if monitoring or reporting is imperfect—clients do not perfectly assess the quality or the reports are lost—a user's rating may not be updated correctly. In the presence of such errors, existing rating mechanisms cannot achieve the social optimum. In this article, we propose the first rating mechanism that does achieve the social optimum, even in the presence of monitoring or reporting errors. On one hand, the socially-optimal rating mechanism needs to be complicated enough, because the optimal recommended behavior depends not only on the current rating distribution, but also (necessarily) on the history of past rating distributions in the platform. On the other hand, we prove that the social optimum can be achieved by “simple” rating mechanisms that use binary rating labels and a small set of (three) recommended behaviors. We provide design guidelines of socially-optimal rating mechanisms and a low-complexity online algorithm for the rating mechanism to determine the optimal recommended behavior.