Flow-based reputation with uncertainty : evidence-based subjective logic

Reputation is widely used in many domains, like electronic commerce, as a measure of trustworthiness based on ratings from members in a community. The adoption of reputation systems, however, relies on their ability to capture the actual trustworthiness of a target. Several reputation models have been proposed in the literature to aggregate trust information. The choice of the type of model has an impact on the reliability of aggregated trust information as well as on the procedure used to compute reputation. Two prominent reputation models are flow-based reputation (e.g. EigenTrust, PageRank) and Subjective Logic based reputation. Flow-based reputation models provide an automated method to aggregate all available trust information, but they are not able to express the level of uncertainty in the aggregated trust information. In contrast, Subjective Logic extends probabilistic models with an explicit notion of uncertainty, but the calculation of reputation depends on the structure of the trust network. In this work we propose a novel reputation model which offers the benefits of both flow-based reputation models and Subjective Logic. In particular, we revisit Subjective Logic and propose a new discounting operator based on the flow of evidence from one party to another. The adoption of our discounting operator results in a Subjective Logic system that is entirely centered on the handling of evidence. We show that this operator enables the construction of an automated reputation assessment procedure for arbitrary trust networks.