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The desirability bias in predictions: Going optimistic without leaving realism

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  • Windschitl, Paul D.
  • Smith, Andrew R.
  • Rose, Jason P.
  • Krizan, Zlatan

Abstract

Does desire for an outcome inflate optimism? Previous experiments have produced mixed results regarding the desirability bias, with the bulk of supportive findings coming from one paradigm--the classic marked-card paradigm in which people make discrete predictions about desirable or undesirable cards being drawn from decks. We introduce a biased-guessing account for the effects from this paradigm, which posits that people are often realistic in their likelihood assessments, but when making a subjectively arbitrary prediction (a guess), they will tend to guess in a desired direction. In order to establish the validity of the biased-guessing account and to distinguish it from other accounts, we conducted five experiments that tested the desirability bias within the paradigm and novel extensions of it. In addition to supporting the biased-guessing account, the findings illustrate the critical role of moderators (e.g., type of outcome, type of forecast) for fully understanding and predicting desirability biases.

Suggested Citation

  • Windschitl, Paul D. & Smith, Andrew R. & Rose, Jason P. & Krizan, Zlatan, 2010. "The desirability bias in predictions: Going optimistic without leaving realism," Organizational Behavior and Human Decision Processes, Elsevier, vol. 111(1), pages 33-47, January.
    • Handle: RePEc:eee:jobhdp:v:111:y:2010:i:1:p:33-47
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    References listed on IDEAS

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    1. van Dijk, Wilco W. & Zeelenberg, Marcel & van der Pligt, Joop, 2003. "Blessed are those who expect nothing: Lowering expectations as a way of avoiding disappointment," Journal of Economic Psychology, Elsevier, vol. 24(4), pages 505-516, August.
    2. Hsee, Christopher K., 1996. "The Evaluability Hypothesis: An Explanation for Preference Reversals between Joint and Separate Evaluations of Alternatives," Organizational Behavior and Human Decision Processes, Elsevier, vol. 67(3), pages 247-257, September.
    3. Isaac M. Lipkus & Greg Samsa & Barbara K. Rimer, 2001. "General Performance on a Numeracy Scale among Highly Educated Samples," Medical Decision Making, , vol. 21(1), pages 37-44, February.
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    Cited by:

    1. Winkler, Jens & Moser, Roger, 2016. "Biases in future-oriented Delphi studies: A cognitive perspective," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 63-76.
    2. Winkler, Jens & Kuklinski, Christian Paul Jian-Wei & Moser, Roger, 2015. "Decision making in emerging markets: The Delphi approach's contribution to coping with uncertainty and equivocality," Journal of Business Research, Elsevier, vol. 68(5), pages 1118-1126.
    3. Ashwani Monga & Haipeng (Allan) Chen & Michael Tsiros & Mona Srivastava, 2012. "How buyers forecast: Buyer–seller relationship as a boundary condition of the impact bias," Marketing Letters, Springer, vol. 23(1), pages 31-45, March.
    4. Windschitl, Paul D. & Scherer, Aaron M. & Smith, Andrew R. & Rose, Jason P., 2013. "Why so confident? The influence of outcome desirability on selective exposure and likelihood judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 120(1), pages 73-86.
    5. Fildes, Robert & Goodwin, Paul & Önkal, Dilek, 2019. "Use and misuse of information in supply chain forecasting of promotion effects," International Journal of Forecasting, Elsevier, vol. 35(1), pages 144-156.

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