Robust Reputation Protocol Design for Online Communities: A Stochastic Stability Analysis

This paper proposes a new class of incentive mechanisms aiming at compelling self-interested users in online communities to cooperate with each other by exchanging resources or services. Examples of such communities are social multimedia platforms, social networks, online labor markets, crowdsourcing platforms, etc. To optimize their individual long-term performance, users adapt their strategies by solving individual stochastic control problems. The users' adaptation catalyze a stochastic dynamic process, in which the strategies of users in the community evolve over time. We first characterize the structural properties of the users' best response strategies. Subsequently, using these structural results we design incentive mechanisms based on reputation protocols for governing the online communities, which can “manage” the long-run evolution of the community. We prove that by appropriately penalizing and rewarding users based on their behavior in the community, such incentive mechanisms can eliminate free-riding and ensure that the community converges to a desirable equilibrium selected by the community designer such that social welfare is maximized and in which users find it in their self-interest to cooperate with each other.