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Hindsight bias redefined: It's about time

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  • Fessel, Florian
  • Epstude, Kai
  • Roese, Neal J.

Abstract

Four experiments introduced a new conceptual and methodological approach to hindsight bias, traditionally defined as the tendency to exaggerate the a priori predictability of outcomes after they become known. By examining likelihood estimates rooted to specific time points during an unfolding event sequence (videos and short text stories), judged both in foresight and hindsight, we conceptualized hindsight bias as a contrast between two "inevitability curves," which plotted likelihood against time. Taking timing into account, we defined three new indicators of accuracy: linear accuracy (how well hindsight judgments capture the linear trend of foresight judgments over time), rate accuracy (how well hindsight judgments reflect the slope of foresight judgments over time), and temporal accuracy (how well hindsight judgments specify the overall timing of the full envelope of foresight judgments). Results demonstrated that hindsight judgments showed linear and rate accuracy, but were biased only in terms of lack of temporal accuracy. The oft-used catchphrase "knew it all along effect" was found to be a misnomer, in that participants were well aware in hindsight that their earlier foresight judgments reflected uncertainty. The current research therefore points to one way in which retrospective judgments can be considered biased, yet simultaneously suggests that considerable accuracy exists when people render such judgments.

Suggested Citation

  • Fessel, Florian & Epstude, Kai & Roese, Neal J., 2009. "Hindsight bias redefined: It's about time," Organizational Behavior and Human Decision Processes, Elsevier, vol. 110(1), pages 56-64, September.
  • Handle: RePEc:eee:jobhdp:v:110:y:2009:i:1:p:56-64
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    References listed on IDEAS

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    1. Müller, Patrick A. & Stahlberg, Dagmar, 2007. "The role of surprise in hindsight bias : a metacognitive model of reduced and reversed hindsight bias," Papers 07-16, Sonderforschungsbreich 504.
    2. Schkade, David A. & Kilbourne, Lynda M., 1991. "Expectation-outcome consistency and hindsight bias," Organizational Behavior and Human Decision Processes, Elsevier, vol. 49(1), pages 105-123, June.
    3. Tetlock, Philip E. & Lebow, Richard Ned, 2001. "Poking Counterfactual Holes in Covering Laws: Cognitive Styles and Historical Reasoning," American Political Science Review, Cambridge University Press, vol. 95(4), pages 829-843, December.
    4. Louie, Therese A., 2005. "Hindsight bias and outcome-consistent thoughts when observing and making service provider decisions," Organizational Behavior and Human Decision Processes, Elsevier, vol. 98(1), pages 88-95, September.
    5. Christensen-Szalanski, Jay J. J. & Willham, Cynthia Fobian, 1991. "The hindsight bias: A meta-analysis," Organizational Behavior and Human Decision Processes, Elsevier, vol. 48(1), pages 147-168, February.
    6. Müller, Patrick A. & Stahlberg, Dagmar, 2007. "The Role of Surprise in Hindsight Bias – A Metacognitive Model of Reduced and Reversed Hindsight Bias," Sonderforschungsbereich 504 Publications 07-16, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
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    Cited by:

    1. Sheng, Liang, 2015. "Dynamic dual process account explaining the bias after outcome – An exploratory research on memory distortion hindsight bias," Working Papers 39948, University of Mannheim, Department of Economics.
    2. Bonaccorsi, Andrea & Apreda, Riccardo & Fantoni, Gualtiero, 2020. "Expert biases in technology foresight. Why they are a problem and how to mitigate them," Technological Forecasting and Social Change, Elsevier, vol. 151(C).

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