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Eliciting Informative Priors by Modeling Expert Decision Making

Author

Listed:
  • Julia R. Falconer

    (Department of Mathematics, University of Waikato, Hamilton 3216, New Zealand; New Zealand Institute of Security and Crime Science, Hamilton 3216, New Zealand)

  • Eibe Frank

    (Department of Computer Science, University of Waikato, Hamilton 3216, New Zealand)

  • Devon L. L. Polaschek

    (New Zealand Institute of Security and Crime Science, Hamilton 3216, New Zealand; School of Psychology, New Zealand Institute of Security and Crime Science, University of Waikato, Hamilton 3216, New Zealand)

  • Chaitanya Joshi

    (New Zealand Institute of Security and Crime Science, Hamilton 3216, New Zealand; Department of Statistics, University of Auckland, Auckland 1142, New Zealand)

Abstract

There are significant limitations to current methods for eliciting the prior beliefs of experts. To combat some of these limitations, this paper proposes an alternative approach that infers an expert’s prior beliefs about an uncertain event, A , from the expert’s past decisions. We show that an analyst can use past information on an expert’s decision-making task, contingent on an expert’s prior of A , to model the decision-making process and infer an approximation of the prior for A . This concept is illustrated by an application to recidivism. We conclude this work by highlighting important directions for future research.

Suggested Citation

  • Julia R. Falconer & Eibe Frank & Devon L. L. Polaschek & Chaitanya Joshi, 2024. "Eliciting Informative Priors by Modeling Expert Decision Making," Decision Analysis, INFORMS, vol. 21(2), pages 77-90, June.
    • Handle: RePEc:inm:ordeca:v:21:y:2024:i:2:p:77-90
    DOI: 10.1287/deca.2023.0046
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    References listed on IDEAS

    as
    1. N. Tollenaar & P. G. M. van der Heijden, 2013. "Which method predicts recidivism best?: a comparison of statistical, machine learning and data mining predictive models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(2), pages 565-584, February.
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    3. Julia R. Falconer & Eibe Frank & Devon L. L. Polaschek & Chaitanya Joshi, 2022. "Methods for Eliciting Informative Prior Distributions: A Critical Review," Decision Analysis, INFORMS, vol. 19(3), pages 189-204, September.
    4. Sean M. O'Brien & David B. Dunson, 2004. "Bayesian Multivariate Logistic Regression," Biometrics, The International Biometric Society, vol. 60(3), pages 739-746, September.
    5. Caulkins, Jonathan & Cohen, Jacqueline & Gorr, Wilpen & Wei, Jifa, 1996. "Predicting criminal recidivism: A comparison of neural network models with statistical methods," Journal of Criminal Justice, Elsevier, vol. 24(3), pages 227-240.
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    8. Rolando de la Cruz & Oslando Padilla & Mauricio A. Valle & Gonzalo A. Ruz, 2021. "Modeling Recidivism through Bayesian Regression Models and Deep Neural Networks," Mathematics, MDPI, vol. 9(6), pages 1-14, March.
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