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Bayesian decision making and military command and control

Author

Listed:
  • J Moffat

    (Dstl Analysis, Defence Science and Technology Laboratory (Dstl))

  • S Witty

    (Dstl Analysis, Defence Science and Technology Laboratory (Dstl))

Abstract

We discuss firstly the problem of military decision, in the context of the more general development of ideas in the representation of decision making. Within this context, we have considered a mathematical model—Bayesian Decision—of decision making and military command. Previous work has been extended, and applied to this problem. A distribution of belief in outcome, given that a decision is made, and a Loss function—a measure of the effect of this outcome relative to a goal—are formed. The Bayes' Decision is the decision which globally minimises the resultant bimodal (or worse) Expected Loss function. The set of all minimising decisions corresponds to the surface of an elementary Catastrophe. This allows smooth parameter changes to lead to a discontinuous change in the Bayes' decision. In future work this approach will be used to help develop a number of hypotheses concerning command processes and military headquarters structure. It will also be used to help capture such command and control processes in simulation modelling of future defence capability and force structure.

Suggested Citation

  • J Moffat & S Witty, 2002. "Bayesian decision making and military command and control," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(7), pages 709-718, July.
    • Handle: RePEc:pal:jorsoc:v:53:y:2002:i:7:d:10.1057_palgrave.jors.2601347
    DOI: 10.1057/palgrave.jors.2601347
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    Citations

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    Cited by:

    1. L Dodd & J Moffat & J Smith, 2006. "Discontinuity in decision-making when objectives conflict: a military command decision case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(6), pages 643-654, June.
    2. J Medhurst & I Stanton & A Berry, 2010. "Risk taking by decision makers—using card-based decision gaming to develop models of behaviour," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(11), pages 1561-1571, November.
    3. Kalloniatis, Alexander C. & McLennan-Smith, Timothy A. & Roberts, Dale O., 2020. "Modelling distributed decision-making in Command and Control using stochastic network synchronisation," European Journal of Operational Research, Elsevier, vol. 284(2), pages 588-603.
    4. J Medhurst & I M Stanton & H Bird & A Berry, 2009. "The value of information to decision makers: an experimental approach using card-based decision gaming," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(6), pages 747-757, June.
    5. Jim Q. Smith & Lorraine Dodd, 2012. "Regulating Autonomous Agents Facing Conflicting Objectives: A Command and Control Example," Decision Analysis, INFORMS, vol. 9(2), pages 165-171, June.
    6. Strong, Peter & Shenvi, Aditi & Yu, Xuewen & Papamichail, K. Nadia & Wynn, Henry P. & Smith, Jim Q., 2023. "Building a Bayesian decision support system for evaluating COVID-19 countermeasure strategies," LSE Research Online Documents on Economics 113632, London School of Economics and Political Science, LSE Library.

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