Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
Managing trust in a peer-2-peer information system
Proceedings of the tenth international conference on Information and knowledge management
Managing and Sharing Servents' Reputations in P2P Systems
IEEE Transactions on Knowledge and Data Engineering
Free-riding and whitewashing in peer-to-peer systems
Proceedings of the ACM SIGCOMM workshop on Practice and theory of incentives in networked systems
Eliciting Truthful Feedback for Binary Reputation Mechanisms
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Effective use of reputation in peer-to-peer environments
CCGRID '04 Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid
An incentives' mechanism promoting truthful feedback in peer-to-peer systems
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid - Volume 01
An Indian logic-based knowledge-sharing architecture for virtual knowledge communities
International Journal of Networking and Virtual Organisations
Argument-based learning communities
Knowledge-Based Systems
A mechanism that provides incentives for truthful feedback in peer-to-peer systems
Electronic Commerce Research
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We analyze a mechanism that provides strong incentives for the submission of truthful feedback in virtual communities where services are exchanged on a peer-to-peer basis. Lying peers are punished with a severity that is exponential to their frequency of lying. We had first introduced and evaluated experimentally the mechanism in [1]. In this paper, we develop a Markov-chain model of the mechanism. Based on this, we prove that, when the mechanism is employed, the system evolves to a beneficial steady-state operation even in the case of a dynamically renewed population. Furthermore, we develop a procedure for the efficient selection of the parameters of the mechanism for any peer-to-peer system; this procedure is based on ergodic arguments. Simulation experiments reveal that the procedure is indeed accurate, as well as effective regarding the incentives provided to participants for submitting truthful feedback.