Sybilproof reputation mechanisms
Proceedings of the 2005 ACM SIGCOMM workshop on Economics of peer-to-peer systems
Minimum payments that reward honest reputation feedback
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IEEE Intelligent Systems
Eliciting Informative Feedback: The Peer-Prediction Method
Management Science
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Complexity of mechanism design
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Enforcing truthful strategies in incentive compatible reputation mechanisms
WINE'05 Proceedings of the First international conference on Internet and Network Economics
Incentives for expressing opinions in online polls
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OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
A Trust-Based Incentive Mechanism for E-Marketplaces
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Mechanisms for making crowds truthful
Journal of Artificial Intelligence Research
Exchanging reputation information between communities: a payment-function approach
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Eliciting honest reputation feedback in a Markov setting
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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Computational Intelligence
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Online reputation mechanisms need honest feedback to function effectively. Self-interested agents report the truth only when explicit rewards offset the potential gains obtained from lying. Feedback payment schemes (monetary rewardsfor submitted feedback) can make truth-telling rational based on the correlation between the reports of different buyers. In this paper we investigate incentive-compatible payment mechanisms that are also resistant to collusion: groups of agents cannot collude on a lying strategy without suffering monetary losses. We analyze several scenarios, where, for example, some or all of the agents collude. For each scenario we investigate both existential and implementation problems. Throughout the paper we use automated mechanism design to compute the best possible mechanism for a given setting.