PRep: a probabilistic reputation model for biased societies

Several reputation models have been introduced to deal with the problem of biased reputation providers. Most of these models discount or discard biased information received from the reputation providers, and most of them are not appropriate when a large population of information providers are biased or dishonest. In this paper, we present a probabilistic approach for reputation modeling, the Probabilistic Reputation model (PRep). PRep models a reputation provider's behavior, and uses this model to re-interpret the reported information, thus making use of the entire reputation reports effectively, even if they are biased. The re-interpreted data is combined with the agent's direct experiences to determine an overall level of trust in the third-party agent. We show that PRep significantly outperforms two state-of-the-art trust and reputation models---HAPTIC and TRAVOS---and improves the overall payoff in a game-theoretic environment.