Rating Protocols in Online Communities

Sustaining cooperation among self-interested agents is critical for the proliferation of emerging online communities. Providing incentives for cooperation in online communities is particularly challenging because of their unique features: a large population of anonymous agents having asymmetric interests and dynamically joining and leaving the community, operation errors, and agents trying to whitewash when they have a low standing in the community. In this article, we take these features into consideration and propose a framework for designing and analyzing a class of incentive schemes based on rating protocols, which consist of a rating scheme and a recommended strategy. We first define the concept of sustainable rating protocols under which every agent has the incentive to follow the recommended strategy given the deployed rating scheme. We then formulate the problem of designing an optimal rating protocol, which selects the protocol that maximizes the overall social welfare among all sustainable rating protocols. Using the proposed framework, we study the structure of optimal rating protocols and explore the impact of one-sided rating, punishment lengths, and whitewashing on optimal rating protocols. Our results show that optimal rating protocols are capable of sustaining cooperation, with the amount of cooperation varying depending on the community characteristics.