Peer-prediction  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||American English|
|Title of host publication||Web and Internet Economics - 12th International Conference, WINE 2016, Proceedings|
|Editors||Adrian Vetta, Yang Cai|
|Number of pages||14|
|State||Published - 2016|
|Event||12th International Conference on Web and Internet Economics, WINE 2016 - Montreal, Canada|
Duration: 11 Jun 2016 → 14 Jul 2016
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||12th International Conference on Web and Internet Economics, WINE 2016|
|Period||11/06/16 → 14/07/16|
Bibliographical noteFunding Information:
K. Ligett—Supported in part NSF grants 1254169 and 1518941, US-Israel Binational Science Foundation Grant 2012348, the Charles Lee Powell Foundation, a subcontract through the Brandeis project, a grant from the HUJI Cyber Security Research Center, and a startup grant from Hebrew Universitys School of Computer Science.
Y. Kong—Supported by National Science Foundation Career Award 1452915 and CCF Award 1618187.
G. Schoenebeck—Supported by National Science Foundation Career Award 1452915 and Algorithms in the Field Award 1535912.
© Springer-Verlag GmbH Germany 2016.
- Information elicitation
- Peer prediction