Eliciting Honest Reputation Feedback in a Markov Setting
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 330-334.
Abstract
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.Jens Witkowski
@inproceedings{ijcai2009p63,
title = {Eliciting Honest Reputation Feedback in a Markov Setting},
author = {Witkowski, Jens},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {330--334},
year = {2009},
url = {https://ijcai.org/proceedings/2009/63},
}
TY - CPAPER TI - Eliciting Honest Reputation Feedback in a Markov Setting AU - Witkowski, Jens T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 330 EP - 334 UR - https://ijcai.org/proceedings/2009/63 ER -Copied
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