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Elicitation in the Classical Model

In: Elicitation

Author

Listed:
  • John Quigley

    (University of Strathclyde)

  • Abigail Colson

    (University of Strathclyde)

  • Willy Aspinall

    (University of Bristol
    Aspinall and Associates)

  • Roger M. Cooke

    (Resources for the Future
    Delft University of Technology)

Abstract

The Classical Model (CM) is a performance-based approach for mathematically aggregating judgements from multiple experts, when reasoning about target questions under uncertainty. Individual expert performance is assessed against a set of seed questions, items from their field, for which the analyst knows or will know the true values, but the experts do not; the experts are, however, expected to provide accurate and informative distributional judgements that capture these values reliably. Performance is measured according to metrics for each expert’s statistical accuracy and informativeness, and the two metrics are convolved to determine a weight for each expert, with which to modulate their contribution when pooling them together for a final combined assessment of the desired target values. This chapter provides mathematical and practical details of the CM, including describing the method for measuring expert performance and discussing approaches for devising good seed questions.

Suggested Citation

  • John Quigley & Abigail Colson & Willy Aspinall & Roger M. Cooke, 2018. "Elicitation in the Classical Model," International Series in Operations Research & Management Science, in: Luis C. Dias & Alec Morton & John Quigley (ed.), Elicitation, chapter 0, pages 15-36, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-65052-4_2
    DOI: 10.1007/978-3-319-65052-4_2
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    Citations

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    Cited by:

    1. 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).
    2. Peter Harrison Howard & Derek Sylvan, 2020. "Wisdom of the experts: Using survey responses to address positive and normative uncertainties in climate-economic models," Climatic Change, Springer, vol. 162(2), pages 213-232, September.
    3. Misuri, Alessio & Landucci, Gabriele & Cozzani, Valerio, 2020. "Assessment of safety barrier performance in Natech scenarios," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    4. Mohammad Yazdi, 2019. "A review paper to examine the validity of Bayesian network to build rational consensus in subjective probabilistic failure analysis," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(1), pages 1-18, February.
    5. Elena Verdolini & Laura Díaz Anadón & Erin Baker & Valentina Bosetti & Lara Aleluia Reis, 2018. "Future Prospects for Energy Technologies: Insights from Expert Elicitations," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 12(1), pages 133-153.
    6. Hathout, Michel & Vuillet, Marc & Carvajal, Claudio & Peyras, Laurent & Diab, Youssef, 2019. "Expert judgments calibration and combination for assessment of river levee failure probability," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 377-392.
    7. Milford, James & Henrion, Max & Hunter, Chad & Newes, Emily & Hughes, Caroline & Baldwin, Samuel F., 2022. "Energy sector portfolio analysis with uncertainty," Applied Energy, Elsevier, vol. 306(PA).
    8. Gayan Dharmarathne & Gabriela F. Nane & Andrew Robinson & Anca M. Hanea, 2023. "Shrinking the Variance in Experts’ “Classical” Weights Used in Expert Judgment Aggregation," Forecasting, MDPI, vol. 5(3), pages 1-14, August.
    9. Schoch-Spana, Monica & Ravi, Sanjana J. & Martin, Elena K., 2022. "Modeling epidemic recovery: An expert elicitation on issues and approaches," Social Science & Medicine, Elsevier, vol. 292(C).
    10. Albara M. Mustafa & Abbas Barabadi & Tore Markeset & Masoud Naseri, 2021. "An overall performance index for wind farms: a case study in Norway Arctic region," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(5), pages 938-950, October.
    11. Jeremy Rohmer & Eric Chojnacki, 2021. "Forecast of environment systems using expert judgements: performance comparison between the possibilistic and the classical model," Environment Systems and Decisions, Springer, vol. 41(1), pages 131-146, March.
    12. Katarina Buganova & Maria Luskova & Jozef Kubas & Michal Brutovsky & Jaroslav Slepecky, 2021. "Sustainability of Business through Project Risk Identification with Use of Expert Estimates," Sustainability, MDPI, vol. 13(11), pages 1-17, June.
    13. Ren, Xin & Nane, Gabriela F. & Terwel, Karel C. & van Gelder, Pieter H.A.J.M., 2024. "Measuring the impacts of human and organizational factors on human errors in the Dutch construction industry using structured expert judgement," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    14. Cooke, Roger M. & Marti, Deniz & Mazzuchi, Thomas, 2021. "Expert forecasting with and without uncertainty quantification and weighting: What do the data say?," International Journal of Forecasting, Elsevier, vol. 37(1), pages 378-387.
    15. Christoph Werner & Tim Bedford & John Quigley, 2018. "Sequential Refined Partitioning for Probabilistic Dependence Assessment," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2683-2702, December.
    16. Wongnak, Phrutsamon & Bord, Séverine & Donnet, Sophie & Hoch, Thierry & Beugnet, Frederic & Chalvet-Monfray, Karine, 2022. "A hierarchical Bayesian approach for incorporating expert opinions into parametric survival models: A case study of female Ixodes ricinus ticks exposed to various temperature and relative humidity con," Ecological Modelling, Elsevier, vol. 464(C).
    17. 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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