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Validation in the Classical Model

In: Elicitation

Author

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  • Roger M. Cooke

    (Resources for the Future
    Delft University of Technology)

Abstract

Validation is the hallmark of science. For expert judgment to contribute to science-based uncertainty quantification, it must become amenable to empirical validation. Using data in which experts quantify uncertainty on variables from their fields whose true values are known post hoc, this chapter explains how validation is performed in the Classical Model for structured expert judgment and reviews results for different combination methods.

Suggested Citation

  • Roger M. Cooke, 2018. "Validation in the Classical Model," International Series in Operations Research & Management Science, in: Luis C. Dias & Alec Morton & John Quigley (ed.), Elicitation, chapter 0, pages 37-59, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-65052-4_3
    DOI: 10.1007/978-3-319-65052-4_3
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    Cited by:

    1. Claire Copeland & Britta Turner & Gareth Powells & Kevin Wilson, 2022. "In Search of Complementarity: Insights from an Exercise in Quantifying Qualitative Energy Futures," Energies, MDPI, vol. 15(15), pages 1-21, July.
    2. 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.
    3. 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).

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