IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v70y2022i6p3558-3580.html
   My bibliography  Save this article

Regularized Aggregation of One-Off Probability Predictions

Author

Listed:
  • Ville A. Satopää

    (Department of Technology and Operations Management, INSEAD, Boulevard de Constance, 77305 Fontainebleau CEDEX, France)

Abstract

Forecasters predicting the chances of a future event may disagree because of differing evidence or noise. To harness the collective evidence of the crowd, we propose a Bayesian aggregator that is regularized by analyzing the forecasters’ disagreement and ascribing overdispersion to noise. Our aggregator requires no user intervention and can be computed efficiently even for a large number of predictions. To illustrate, we evaluate our aggregator on subjective probability predictions collected during a four-year forecasting tournament sponsored by the U.S. intelligence community. Our aggregator improves the squared error (a.k.a., the Brier score) of simple averaging by around 20% and other commonly used aggregators by 10%–25%. This advantage stems almost exclusively from improved calibration. An R package called braggR implements our method and is available on CRAN.

Suggested Citation

  • Ville A. Satopää, 2022. "Regularized Aggregation of One-Off Probability Predictions," Operations Research, INFORMS, vol. 70(6), pages 3558-3580, November.
    • Handle: RePEc:inm:oropre:v:70:y:2022:i:6:p:3558-3580
    DOI: 10.1287/opre.2021.2224
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.2021.2224
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.2021.2224?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:oropre:v:70:y:2022:i:6:p:3558-3580. 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.