User adaptation and long-run evolution in online communities

Abstract

This paper focuses on analyzing the interactions emerging between users in online communities. Network utility maximization and other methods are not effective when the communities are composed of intelligent and self-interested users (multimedia social communities, social networks etc.), because the interests of the individual users may be in conflict. In our prior work, we propose to design protocols in a stationary community to provide users incentives to voluntarily operate according to pre-determined social norms and provide services. In this paper, we extend the study to analyze the interactions of self-interested users under a social norm in an online community of finite population, where the stationary property of the community does not hold. To optimize their long-term performance based on their knowledge, users adapt strategies to play their best response by solving individual stochastic control problems. Understanding the evolution of a community provides protocol designers guidelines for designing social norms in which no user will have the incentive to adapt and deviate from the prescribed protocol, which in turn encourages cooperative behavior among users and achieves the optimal social welfare of the community.