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Bayesian policy support for adaptive strategies using computer models for complex physical systems

Author

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  • D Williamson

    (Durham University, Durham, UK)

  • M Goldstein

    (Durham University, Durham, UK)

Abstract

In this paper, we discuss combining expert knowledge and computer simulators in order to provide decision support for policy makers managing complex physical systems. We allow future states of the complex system to be viewed after initial policy is made, and for those states to influence revision of policy. The potential for future observations and intervention impacts heavily on optimal policy for today and this is handled within our approach. We show how deriving policy dependent system uncertainty using computer models leads to an intractable backwards induction problem for the resulting decision tree. We introduce an algorithm for emulating an upper bound on our expected loss surface for all possible policies and discuss how this might be used in policy support. To illustrate our methodology, we look at choosing an optimal CO2 abatement strategy, combining an intermediate complexity climate model and an economic utility model with climate data.

Suggested Citation

  • D Williamson & M Goldstein, 2012. "Bayesian policy support for adaptive strategies using computer models for complex physical systems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(8), pages 1021-1033, August.
    • Handle: RePEc:pal:jorsoc:v:63:y:2012:i:8:p:1021-1033
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    Cited by:

    1. Simon French & Nikolaos Argyris & Stephanie M. Haywood & Matthew C. Hort & Jim Q. Smith, 2019. "Communicating Geographical Risks in Crisis Management: The Need for Research," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 9-16, January.
    2. Rebecca E. Atanga & Edward L. Boone & Ryad A. Ghanam & Ben Stewart-Koster, 2021. "Optimal Sampling Regimes for Estimating Population Dynamics," Stats, MDPI, vol. 4(2), pages 1-17, April.
    3. Simon French & Nikolaos Argyris, 2018. "Decision Analysis and Political Processes," Decision Analysis, INFORMS, vol. 15(4), pages 208-222, December.

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