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Informed Truthfulness in Multi-Task Peer Prediction

Shnayder, Victor and Agarwal, Arpit and Frongillo, Rafael and Parkes, David C (2016) Informed Truthfulness in Multi-Task Peer Prediction. In: 17th ACM Conference on Economics and Computation (EC), JUL 24-28, 2016, Maastricht, NETHERLANDS, pp. 179-196.

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Official URL: https://arxiv.org/pdf/1603.03151v2.pdf

Abstract

The problem of peer prediction is to elicit information from agents in settings without any objective ground truth against which to score reports. Peer prediction mechanisms seek to exploit correlations between signals to align incentives with truthful reports. A long-standing concern has been the possibility of uninformative equilibria. For binary signals, a multi-task mechanism Dasgupta and Ghosh 2013] achieves strong truthfulness, so that the truthful equilibrium strictly maximizes payoff. We characterize conditions on the signal distribution for which this mechanism remains strongly-truthful with non-binary signals, also providing a greatly simplified proof. We introduce the Correlated Agreement (CA) mechanism, which handles multiple signals and provides informed truthfulness: no strategy profile provides more payoff in equilibrium than truthful reporting, and the truthful equilibrium is strictly better than any uninformed strategy (where an agent avoids the effort of obtaining a signal). The CA mechanism is maximally strongly truthful, in that no mechanism in a broad class of mechanisms is strongly truthful on a larger family of signal distributions. We also give a detail-free version of the mechanism that removes any knowledge requirements on the part of the designer, using reports on many tasks to learn statistics while retaining epsilon-informed truthfulness.

Item Type: Conference Paper
Additional Information: Copy right for this article belongs to the ASSOC COMPUTING MACHINERY, 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Depositing User: Id for Latest eprints
Date Deposited: 03 Dec 2016 10:22
Last Modified: 03 Dec 2016 10:22
URI: http://eprints.iisc.ac.in/id/eprint/55436

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