IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v70y2024i9p5931-5948.html
   My bibliography  Save this article

A Bayesian Hierarchical Model of Crowd Wisdom Based on Predicting Opinions of Others

Author

Listed:
  • John McCoy

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Drazen Prelec

    (Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139; Department of Economics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139; Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

In many domains, it is necessary to combine opinions or forecasts from multiple individuals. However, the average or modal judgment is often incorrect, shared information across respondents can result in correlated errors, and weighting judgments by confidence does not guarantee accuracy. We develop a Bayesian hierarchical model of crowd wisdom that incorporates predictions about others to address these aggregation challenges. The proposed model can be applied to single questions, and it can also estimate respondent expertise given multiple questions. Unlike existing Bayesian hierarchical models for aggregation, the model does not link the correct answer to consensus or privilege majority opinion. The model extends the “surprisingly popular algorithm” to enable statistical inference and in doing so, overcomes several of its limitations. We assess performance on empirical data and compare the results with other aggregation methods, including leading Bayesian hierarchical models.

Suggested Citation

  • John McCoy & Drazen Prelec, 2024. "A Bayesian Hierarchical Model of Crowd Wisdom Based on Predicting Opinions of Others," Management Science, INFORMS, vol. 70(9), pages 5931-5948, September.
    • Handle: RePEc:inm:ormnsc:v:70:y:2024:i:9:p:5931-5948
    DOI: 10.1287/mnsc.2023.4955
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.2023.4955
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.2023.4955?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:70:y:2024:i:9:p:5931-5948. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.