A Robust Bayesian Truth Serum for Small Populations

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A Robust Bayesian Truth Serum for Small Populations

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Title: A Robust Bayesian Truth Serum for Small Populations
Author: Parkes, David C.; Witkowski, Jens

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Citation: Witkowski, Jens and David C. Parkes. 2012. "A robust Bayesian truth serum for small populations." In Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI'12), July 22–26, 2012, Toronto, Ontario, Canada.
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Abstract: Peer prediction mechanisms allow the truthful elicitation of private signals (e.g., experiences, or opinions) in regard to a true world state when this ground truth is unobservable. The original peer prediction method is incentive compatible for any number of agents n >= 2, but relies on a common prior, shared by all agents and the mechanism. The Bayesian Truth Serum (BTS) relaxes this assumption. While BTS still assumes that agents share a common prior, this prior need not be known to the mechanism. However, BTS is only incentive compatible for a large enough number of agents, and the particular number of agents required is uncertain because it depends on this private prior. In this paper, we present a robust BTS for the elicitation of binary information which is incentive compatible for every n >= 3, taking advantage of a particularity of the quadratic scoring rule. The robust BTS is the first peer prediction mechanism to provide strict incentive compatibility for every n >= 3 without relying on knowledge of the common prior. Moreover, and in contrast to the original BTS, our mechanism is numerically robust and ex post individually rational.
Published Version: http://www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/view/5096
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:11882034
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