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Eliciting Informative Feedback: The Peer-Prediction Method

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Author Info

  • Nolan Miller

    ()
    (Kennedy School of Government, Harvard University, Cambridge, Massachusetts 02138)

  • Paul Resnick

    ()
    (School of Information, University of Michigan, Ann Arbor, Michigan 48109-1092)

  • Richard Zeckhauser

    ()
    (Kennedy School of Government, Harvard University, Cambridge, Massachusetts 02138)

Abstract

Many recommendation and decision processes depend on eliciting evaluations of opportunities, products, and vendors. A scoring system is devised that induces honest reporting of feedback. Each rater merely reports a signal, and the system applies proper scoring rules to the implied posterior beliefs about another rater's report. Honest reporting proves to be a Nash equilibrium. The scoring schemes can be scaled to induce appropriate effort by raters and can be extended to handle sequential interaction and continuous signals. We also address a number of practical implementation issues that arise in settings such as academic reviewing and online recommender and reputation systems.

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File URL: http://dx.doi.org/10.1287/mnsc.1050.0379
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Bibliographic Info

Article provided by INFORMS in its journal Management Science.

Volume (Year): 51 (2005)
Issue (Month): 9 (September)
Pages: 1359-1373

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Handle: RePEc:inm:ormnsc:v:51:y:2005:i:9:p:1359-1373

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Related research

Keywords: proper scoring rules; electronic markets; honest feedback;

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Cited by:
  1. Weijia Dai & Ginger Z. Jin & Jungmin Lee & Michael Luca, 2012. "Optimal Aggregation of Consumer Ratings: An Application to Yelp.com," NBER Working Papers 18567, National Bureau of Economic Research, Inc.
  2. Aperjis, Christina & Miao, Yali & Zeckhauser, Richard J., 2011. "Variable Temptations and Black Market Reputations," Working Paper Series 11-020, Harvard University, John F. Kennedy School of Government.
  3. Karl Schlag & James Tremewan & Joel van der Weele, 2014. "A Penny for Your Thoughts:A Survey of Methods for Eliciting Beliefs," Vienna Economics Papers 1401, University of Vienna, Department of Economics.
  4. Alessandro Acquisti, 2014. "Inducing Customers to Try New Goods," Review of Industrial Organization, Springer, vol. 44(2), pages 131-146, March.
  5. Peysakhovich, Alexander & Plagborg-Møller, Mikkel, 2012. "A note on proper scoring rules and risk aversion," Economics Letters, Elsevier, vol. 117(1), pages 357-361.
  6. Benjamin Van Roy & Xiang Yan, 2009. "Manipulation Robustness of Collaborative Filtering Systems," Working Papers 09-21, NET Institute, revised Sep 2009.

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