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Eliciting Probabilistic Judgements for Integrating Decision Support Systems

In: Elicitation

Author

Listed:
  • Martine J. Barons

    (University of Warwick)

  • Sophia K. Wright

    (University of Warwick)

  • Jim Q. Smith

    (University of Warwick
    Alan Turing Institute)

Abstract

When facing extremely large and interconnected systems, decision-makers must often combine evidence obtained from multiple expert domains, each informed by a distinct panel of experts. To guide this combination so that it takes place in a coherent manner, we need an integrating decision support system (IDSS). This enables the user to calculate the subjective expected utility scores of candidate policies as well as providing a framework for incorporating measures of uncertainty into the system. Throughout this chapter we justify and describe the use of IDSS models and how this procedure is being implemented to inform decision-making for policies impacting food poverty within the UK. In particular, we provide specific details of this elicitation process when the overarching framework of the IDSS is a dynamic Bayesian network (DBN).

Suggested Citation

  • Martine J. Barons & Sophia K. Wright & Jim Q. Smith, 2018. "Eliciting Probabilistic Judgements for Integrating Decision Support Systems," International Series in Operations Research & Management Science, in: Luis C. Dias & Alec Morton & John Quigley (ed.), Elicitation, chapter 0, pages 445-478, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-65052-4_17
    DOI: 10.1007/978-3-319-65052-4_17
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    Cited by:

    1. 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.
    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.

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