Iterative combinatorial auctions: achieving economic and computational efficiency
Iterative combinatorial auctions: achieving economic and computational efficiency
Minimum payments that reward honest reputation feedback
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Eliciting Informative Feedback: The Peer-Prediction Method
Management Science
Collusion-resistant, incentive-compatible feedback payments
Proceedings of the 8th ACM conference on Electronic commerce
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
Peer prediction without a common prior
Proceedings of the 13th ACM Conference on Electronic Commerce
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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Recently, online reputation mechanisms have been proposed that reward agents for honest feedback about products and services with fixed quality. Many real-world settings, however, are inherently dynamic. As an example, consider a web service that wishes to publish the expected download speed of a file mirrored on different server sites. In contrast to the models of Miller, Resnick and Zeckhauser and of Jurca and Faltings, the quality of the service (e. g., a server's available bandwidth) changes over time and future agents are solely interested in the present quality levels. We show that hidden Markov models (HMM) provide natural generalizations of these static models and design a payment scheme that elicits honest reports from the agents after they have experienced the quality of the service.