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The ‘heuristics and biases’ bias in expert elicitation

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  • Mary Kynn

Abstract

Summary. In the early 1970s Tversky and Kahneman published a series of papers on ‘heuristics and biases’ describing human inadequacies in assessing probabilities, culminating in a highly popular article in Science. This seminal research has been heavily cited in many fields, including statistics, as the definitive research on probability assessment. Curiously, although this work was debated at the time and more recent work has largely refuted many of the claims, this apparent heuristics and biases bias in elicitation research has gone unremarked. Over a decade of research into the frequency effect, the importance of framing, and cognitive models more generally, has been almost completely ignored by the statistical literature on expert elicitation. To remedy this situation, this review offers a guide to the psychological research on assessing probabilities, both old and new, and gives concrete guidelines for eliciting expert knowledge.

Suggested Citation

  • Mary Kynn, 2008. "The ‘heuristics and biases’ bias in expert elicitation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 239-264, January.
  • Handle: RePEc:bla:jorssa:v:171:y:2008:i:1:p:239-264
    DOI: 10.1111/j.1467-985X.2007.00499.x
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    Cited by:

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    2. Flandoli, F. & Giorgi, E. & Aspinall, W.P. & Neri, A., 2011. "Comparison of a new expert elicitation model with the Classical Model, equal weights and single experts, using a cross-validation technique," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1292-1310.
    3. Mkrtchyan, L. & Podofillini, L. & Dang, V.N., 2015. "Bayesian belief networks for human reliability analysis: A review of applications and gaps," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 1-16.
    4. Johan Ullberg & Pontus Johnson, 2017. "Empirical assessment of the accuracy of an interoperability prediction language," Information Systems Frontiers, Springer, vol. 19(4), pages 819-833, August.
    5. Hosack, Geoffrey R. & Hayes, Keith R. & Barry, Simon C., 2017. "Prior elicitation for Bayesian generalised linear models with application to risk control option assessment," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 351-361.
    6. Fredrik Carlsson & Dinky Daruvala & Henrik Jaldell, 2012. "Do administrators have the same priorities for risk reductions as the general public?," Journal of Risk and Uncertainty, Springer, vol. 45(1), pages 79-95, August.
    7. Berrang-Ford, Lea & Garton, Kelly, 2013. "Expert knowledge sourcing for public health surveillance: National tsetse mapping in Uganda," Social Science & Medicine, Elsevier, vol. 91(C), pages 246-255.
    8. Brito, Mario & Griffiths, Gwyn, 2016. "A Bayesian approach for predicting risk of autonomous underwater vehicle loss during their missions," Reliability Engineering and System Safety, Elsevier, vol. 146(C), pages 55-67.
    9. Johan Ullberg & Pontus Johnson, 0. "Empirical assessment of the accuracy of an interoperability prediction language," Information Systems Frontiers, Springer, vol. 0, pages 1-15.
    10. Roopesh Ranjan & Tilmann Gneiting, 2010. "Combining probability forecasts," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 71-91, January.
    11. Scholten, Lisa & Schuwirth, Nele & Reichert, Peter & Lienert, Judit, 2015. "Tackling uncertainty in multi-criteria decision analysis – An application to water supply infrastructure planning," European Journal of Operational Research, Elsevier, vol. 242(1), pages 243-260.
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