IDEAS home Printed from https://ideas.repec.org/a/sae/simgam/v47y2016i6p751-779.html
   My bibliography  Save this article

Training Anchoring and Representativeness Bias Mitigation Through a Digital Game

Author

Listed:
  • Yu-Hao Lee
  • Norah E. Dunbar
  • Claude H. Miller
  • Brianna L. Lane
  • Matthew L. Jensen
  • Elena Bessarabova
  • Judee K. Burgoon
  • Bradley J. Adame
  • Joseph J. Valacich
  • Elissa A. Adame
  • Eryn Bostwick
  • Cameron W. Piercy
  • Javier Elizondo
  • Scott N. Wilson

Abstract

Objective. Humans systematically make poor decisions because of cognitive biases. Can digital games train people to avoid cognitive biases? The goal of this study is to investigate the affordance of different educational media in training people about cognitive biases and to mitigate cognitive biases within their decision-making processes. Method. A between-subject experiment was conducted to compare a digital game, a traditional slideshow, and a combined condition in mitigating two types of cognitive biases: anchoring bias and representativeness bias. We measured both immediate effects and delayed effects after four weeks. Results. The digital game and slideshow conditions were effective in mitigating cognitive biases immediately after the training, but the effects decayed after four weeks. By providing the basic knowledge through the slideshow, then allowing learners to practice bias-mitigation techniques in the digital game, the combined condition was most effective at mitigating the cognitive biases both immediately and after four weeks.

Suggested Citation

  • Yu-Hao Lee & Norah E. Dunbar & Claude H. Miller & Brianna L. Lane & Matthew L. Jensen & Elena Bessarabova & Judee K. Burgoon & Bradley J. Adame & Joseph J. Valacich & Elissa A. Adame & Eryn Bostwick &, 2016. "Training Anchoring and Representativeness Bias Mitigation Through a Digital Game," Simulation & Gaming, , vol. 47(6), pages 751-779, December.
  • Handle: RePEc:sae:simgam:v:47:y:2016:i:6:p:751-779
    DOI: 10.1177/1046878116662955
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1046878116662955
    Download Restriction: no

    File URL: https://libkey.io/10.1177/1046878116662955?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:sae:simgam:v:47:y:2016:i:6:p:751-779. 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: SAGE Publications (email available below). General contact details of provider: .

    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.