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Sensitivity analysis for decision-making using the MORE method—A Pareto approach

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  • Ravalico, Jakin K.
  • Maier, Holger R.
  • Dandy, Graeme C.

Abstract

Integrated Assessment Modelling (IAM) incorporates knowledge from different disciplines to provide an overarching assessment of the impact of different management decisions. The complex nature of these models, which often include non-linearities and feedback loops, requires special attention for sensitivity analysis. This is especially true when the models are used to form the basis of management decisions, where it is important to assess how sensitive the decisions being made are to changes in model parameters. This research proposes an extension to the Management Option Rank Equivalence (MORE) method of sensitivity analysis; a new method of sensitivity analysis developed specifically for use in IAM and decision-making. The extension proposes using a multi-objective Pareto optimal search to locate minimum combined parameter changes that result in a change in the preferred management option. It is demonstrated through a case study of the Namoi River, where results show that the extension to MORE is able to provide sensitivity information for individual parameters that takes into account simultaneous variations in all parameters. Furthermore, the increased sensitivities to individual parameters that are discovered when joint parameter variation is taken into account shows the importance of ensuring that any sensitivity analysis accounts for these changes.

Suggested Citation

  • Ravalico, Jakin K. & Maier, Holger R. & Dandy, Graeme C., 2009. "Sensitivity analysis for decision-making using the MORE method—A Pareto approach," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1229-1237.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:7:p:1229-1237
    DOI: 10.1016/j.ress.2009.01.009
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    1. Insua, David Rios & French, Simon, 1991. "A framework for sensitivity analysis in discrete multi-objective decision-making," European Journal of Operational Research, Elsevier, vol. 54(2), pages 176-190, September.
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    1. Hurford, A.P. & Harou, J.J. & Bonzanigo, L. & Ray, P.A. & Karki, P. & Bharati, L. & Chinnasamy, P., 2020. "Efficient and robust hydropower system design under uncertainty - A demonstration in Nepal," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).

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