Abstract
Peer-prediction [19] is a (meta-)mechanism which, given any proper scoring rule, produces a mechanism to elicit prie information from self-interested agents. Formally, truth-telling is a strict Nash equilibrium of the mechanism. Unfortunately, there may be other equilibria as well (including uninformative equilibria where all players simply report the same fixed signal, regardless of their true signal) and, typically, the truthtelling equilibrium does not have the highest expected payoff. The main result of this paper is to show that, in the symmetric binary setting, by tweaking peer-prediction, in part by carefully selecting the proper scoring rule it is based on, we can make the truth-telling equilibrium focal—that is, truth-telling has higher expected payoff than any other equilibrium. Along the way, we prove the following: in the setting where agents receive binary signals we (1) classify all equilibria of the peer-prediction mechanism; (2) introduce a new technical tool for understanding scoring rules, which allows us to make truth-telling pay better than any other informative equilibrium; (3) leverage this tool to provide an optimal version of the previous result; that is, we optimize the gap between the expected payoff of truth-telling and other informative equilibria; and (4) show that with a slight modification to the peer-prediction framework, we can, in general, make the truth-telling equilibrium focal—that is, truth-telling pays more than any other equilibrium (including the uninformative equilibria).
Original language | English |
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Title of host publication | Web and Internet Economics - 12th International Conference, WINE 2016, Proceedings |
Editors | Adrian Vetta, Yang Cai |
Publisher | Springer Verlag |
Pages | 251-264 |
Number of pages | 14 |
ISBN (Print) | 9783662541098 |
DOIs | |
State | Published - 2016 |
Event | 12th International Conference on Web and Internet Economics, WINE 2016 - Montreal, Canada Duration: 11 Jun 2016 → 14 Jul 2016 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10123 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 12th International Conference on Web and Internet Economics, WINE 2016 |
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Country/Territory | Canada |
City | Montreal |
Period | 11/06/16 → 14/07/16 |
Bibliographical note
Publisher Copyright:© Springer-Verlag GmbH Germany 2016.
Keywords
- Crowdsourcing
- Information elicitation
- Peer prediction