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Eliciting Informative Feedback: The Peer-Prediction Method

Author

Listed:
  • 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.

Suggested Citation

  • Nolan Miller & Paul Resnick & Richard Zeckhauser, 2005. "Eliciting Informative Feedback: The Peer-Prediction Method," Management Science, INFORMS, vol. 51(9), pages 1359-1373, September.
    • Handle: RePEc:inm:ormnsc:v:51:y:2005:i:9:p:1359-1373
    DOI: 10.1287/mnsc.1050.0379
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    References listed on IDEAS

    as
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