Reputation design for adaptive networks with selfish agents

We consider a general information-sharing game over adaptive networks with selfish agents, in which a diffusion strategy is employed to estimate a common target parameter. The benefit and cost of sharing information are embedded into the individual utility functions. We formulate the interactions among selfish agents as successive one-shot games and show that the dominant strategy is for agents not to share information with each other. In order to encourage cooperation among selfish agents, we design a reputation scheme that enables agents to utilize the historic summary of other agents' past actions to predict future returns that would result from being cooperative i.e., from sharing information with other agents. Simulations illustrate the benefits of the combined diffusion and reputation strategies for learning over networks with selfish agents.