Putting peer prediction under the micro(Economic)scope and making truth-telling focal

Yuqing Kong*, Katrina Ligett, Grant Schoenebeck

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

23 Scopus citations

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 languageAmerican English
Title of host publicationWeb and Internet Economics - 12th International Conference, WINE 2016, Proceedings
EditorsAdrian Vetta, Yang Cai
PublisherSpringer Verlag
Pages251-264
Number of pages14
ISBN (Print)9783662541098
DOIs
StatePublished - 2016
Event12th International Conference on Web and Internet Economics, WINE 2016 - Montreal, Canada
Duration: 11 Jun 201614 Jul 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10123 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Web and Internet Economics, WINE 2016
Country/TerritoryCanada
CityMontreal
Period11/06/1614/07/16

Bibliographical note

Publisher Copyright:
© Springer-Verlag GmbH Germany 2016.

Keywords

  • Crowdsourcing
  • Information elicitation
  • Peer prediction

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