Communications of the ACM
Reputation systems: an axiomatic approach
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Ranking systems: the PageRank axioms
Proceedings of the 6th ACM conference on Electronic commerce
The Role of Reputation Systems in Reducing On-Line Auction Fraud
International Journal of Electronic Commerce
Effects of a reputation feedback system on an online consumer-to-consumer auction market
Decision Support Systems
Online reputation systems: Design and strategic practices
Decision Support Systems
Assessing Robustness of Reputation Systems Regarding Interdependent Manipulations
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
An axiomatic approach to personalized ranking systems
Journal of the ACM (JACM)
A mechanism that provides incentives for truthful feedback in peer-to-peer systems
Electronic Commerce Research
Trust mechanisms for online systems
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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This paper proposes a novel mechanism for inducing cooperation in online auction settings with noisy monitoring of quality and adverse selection. The mechanism combines the ability of electronic markets to solicit feedback from buyers with the more traditional ability to levy listing fees from sellers. Each period the mechanism charges a listing fee contingent on a seller's announced expected quality. It subsequently pays the seller a reward contingent on both his announced quality and the rating posted for that seller by that period's winning bidder. I show that, in the presence of a continuum of seller types with different cost functions, imperfect private monitoring of a seller's effort level and a simple "binary" feedback mechanism that asks buyers to rate a transaction as "good" or "bad", it is possible to derive a schedule of fees and rewards that induces all seller types to produce at their respective first-best quality levels and to truthfully announce their intended quality levels to buyers. The mechanism maximizes average social welfare for the entire community and is robust to a number of contingencies of particular concern in online environments, such as easy name changes and the existence of inept sellers. On the other hand, the mechanism distorts the resulting payoffs of individual sellers relative to the complete information case, transferring part of the payoffs of more efficient sellers to less efficient sellers. The magnitude of this distortion is proportional to the amount of noise associated with observing and reporting the quality of a good.