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Bayesian decision support for complex systems with many distributed experts

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  • Manuele Leonelli
  • James Smith

Abstract

Complex decision support systems often consist of component modules which, encoding the judgements of panels of domain experts, describe a particular sub-domain of the overall system. Ideally these modules need to be pasted together to provide a comprehensive picture of the whole process. The challenge of building such an integrated system is that, whilst the overall qualitative features are common knowledge to all, the explicit forecasts and their associated uncertainties are only expressed individually by each panel, resulting from its own analysis. The structure of the integrated system therefore needs to facilitate the coherent piecing together of these separate evaluations. If such a system is not available there is a serious danger that this might drive decision makers to incoherent and so indefensible policy choices. In this paper we develop a graphically based framework which embeds a set of conditions, consisting of the agreement usually made in practice of certain probability and utility models, that, if satisfied in a given context, are sufficient to ensure the composite system is truly coherent. Furthermore, we develop new message passing algorithms entailing the transmission of expected utility scores between the panels, that enable the uncertainties within each module to be fully accounted for in the evaluation of the available alternatives in these composite systems. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Manuele Leonelli & James Smith, 2015. "Bayesian decision support for complex systems with many distributed experts," Annals of Operations Research, Springer, vol. 235(1), pages 517-542, December.
    • Handle: RePEc:spr:annopr:v:235:y:2015:i:1:p:517-542:10.1007/s10479-015-1957-7
    DOI: 10.1007/s10479-015-1957-7
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    References listed on IDEAS

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    Cited by:

    1. Simon French, 2024. "Whose Judgement? Reflections on Elicitation in Bayesian Analysis," Decision Analysis, INFORMS, vol. 21(3), pages 143-159, September.
    2. Robin L. Dillon & Vicki M. Bier & Richard Sheffield John & Abdullah Althenayyan, 2023. "Closing the Gap Between Decision Analysis and Policy Analysts Before the Next Pandemic," Decision Analysis, INFORMS, vol. 20(2), pages 109-132, June.
    3. Manuele Leonelli & Jim Q. Smith, 2017. "Directed Expected Utility Networks," Decision Analysis, INFORMS, vol. 14(2), pages 108-125, June.
    4. Manuele Leonelli & Eva Riccomagno & Jim Q. Smith, 2020. "Coherent combination of probabilistic outputs for group decision making: an algebraic approach," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(2), pages 499-528, June.
    5. Andrew Gelman & Christian Hennig, 2017. "Beyond subjective and objective in statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 967-1033, October.
    6. Martine J. Barons & Thais C. O. Fonseca & Andy Davis & Jim Q. Smith, 2022. "A decision support system for addressing food security in the United Kingdom," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 447-470, April.
    7. Panula-Ontto, Juha, 2019. "The AXIOM approach for probabilistic and causal modeling with expert elicited inputs," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 292-308.

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