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An approach to perform expert elicitation for engineering design risk analysis: methodology and experimental results

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  • Alessandra Babuscia
  • Kar-Ming Cheung

Abstract

type="main" xml:id="rssa12028-abs-0001"> Expert elicitation is increasingly applied to different research areas. Multiple approaches have been implemented, but the development of methods to quantify experts' biases and calibration represents a challenge. As a result, the integration of multiple and often conflicting opinions can be demanding, owing to the complexity of properly weighting experts' contributions. We propose an approach to address this problem when probability densities for seed calibration variables are not available. The methodology generates an expert score that is employed to aggregate multiple-expert assessments. The approach has been experimentally applied to engineering design risk analysis. Results indicate that the approach improves the quality of the estimations. The weighted aggregations of experts' estimates based on the experts' scores achieve better results than the corresponding aggregations based on experts' opinions equally weighted.

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  • Alessandra Babuscia & Kar-Ming Cheung, 2014. "An approach to perform expert elicitation for engineering design risk analysis: methodology and experimental results," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(2), pages 475-497, February.
    • Handle: RePEc:bla:jorssa:v:177:y:2014:i:2:p:475-497
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    File URL: http://hdl.handle.net/10.1111/rssa.2014.177.issue-2
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    Cited by:

    1. Wilson, Kevin J., 2017. "An investigation of dependence in expert judgement studies with multiple experts," International Journal of Forecasting, Elsevier, vol. 33(1), pages 325-336.
    2. Carless, Travis S. & Redus, Kenneth & Dryden, Rachel, 2021. "Estimating nuclear proliferation and security risks in emerging markets using Bayesian Belief Networks," Energy Policy, Elsevier, vol. 159(C).
    3. Cameron J. Williams & Kevin J. Wilson & Nina Wilson, 2021. "A comparison of prior elicitation aggregation using the classical method and SHELF," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 920-940, July.

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