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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.
A Robust Bayesian Truth Serum for Small Populations J. Witkowski, D. C. ParkesIn Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI'12), 2012
Bibtex Entry:
  author =	 {Witkowski, Jens and Parkes, David C.},
  title =	 {{A Robust Bayesian Truth Serum for Small Populations}},
  booktitle =	 {Proceedings of the 26th AAAI Conference on
                  Artificial Intelligence (AAAI'12)},
  year =	 {2012},
pages = {1492--1498},
  month = 	 {July},