Influencing the long-term evolution of online communities using social norms

This paper focuses on analyzing the interactions emerging between users in online communities. Network utility maximization and other methods can be used to achieve efficient designs when the communities are composed of compliant users. However, such methods are not effective and efficient 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 designed social reciprocation protocols by assuming a stationary community in which a continuum population interacts. We proved that given these assumptions, users have incentives to voluntarily operate according to pre-determined social norms and provide services. In this paper, we extend this study to analyze the interactions of self-interested users under a social norm in an online community of finite population and without making stationary assumptions about the community. To optimize their long-term performance while operating in the community, users adapt strategies to play their best response based on their knowledge by solving individual stochastic control problems. The best-response dynamic introduces a stochastic dynamic process in the community, in which the strategies of users evolve over time. Understanding how a community responds to incentives in the long- term provides protocol designers with guidelines for designing social norms in which no user will find it into its self-interest to adapt and deviate from the prescribed protocol. This will, in turn, influence the evolution of the community and induce the emergence of cooperative behavior among users, thereby maximizing the optimal social welfare of the community.