IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v41y2025i2p487-498.html
   My bibliography  Save this article

How does training improve individual forecasts? Modeling differences in compensatory and non-compensatory biases in geopolitical forecasts

Author

Listed:
  • Karimi Motahhar, Vahid
  • Gruca, Thomas S.

Abstract

Biases in human forecasters lead to poor calibration. We assess how formal training affects two types of bias in probabilistic forecasts of binary outcomes. Compensatory bias occurs when underestimation in one range of probabilities (e.g., less than 50%) is accompanied by overestimation in the opposite range. Non-compensatory bias occurs when the direction of misestimation is consistent throughout the entire range of probabilities. We present a new approach to modeling probabilistic forecasts to determine the extent and direction of compensatory and non-compensatory biases. Using data from the Good Judgment Project, we model the effects of training (randomly assigned) on the calibration of 39,481 initial forecasts from 851 forecasters across two years of the contest. The forecasts exhibit significant indications of both compensatory and non-compensatory biases across all forecasters. Training significantly reduces the compensatory bias in both years. It reduces the non-compensatory bias only in the second year of the contest.

Suggested Citation

  • Karimi Motahhar, Vahid & Gruca, Thomas S., 2025. "How does training improve individual forecasts? Modeling differences in compensatory and non-compensatory biases in geopolitical forecasts," International Journal of Forecasting, Elsevier, vol. 41(2), pages 487-498.
    • Handle: RePEc:eee:intfor:v:41:y:2025:i:2:p:487-498
    DOI: 10.1016/j.ijforecast.2024.12.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207024001298
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2024.12.001?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:eee:intfor:v:41:y:2025:i:2:p:487-498. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    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.