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.

@INPROCEEDINGS{witkowski:2009a,
  author =	 {Witkowski, Jens},
  title =	 {{Eliciting Honest Reputation Feedback in a Markov
                  Setting}},
  booktitle =	 {Proceedings of the 21st International Joint
                  Conference on Artificial Intelligence (IJCAI'09)},
  year =	 {2009},
  pages =        {330--335},
  month = 	 {July},
}