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

机译:使用MORE方法-Pareto方法进行决策的敏感性分析

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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 t;ue 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.
机译:综合评估模型(IAM)融合了来自不同学科的知识,以提供对不同管理决策影响的总体评估。这些模型的复杂性通常包括非线性和反馈回路,因此需要特别注意进行敏感性分析。当使用模型作为管理决策的基础时,评估模型的敏感性非常重要。决策是要更改模型参数。本研究提出了敏感性分析的管理期权等级等价(MORE)方法的扩展。专为IAM和决策制定的一种新的敏感性分析方法。该扩展建议使用多目标帕累托最优搜索来定位最小组合参数更改,这些更改会导致首选管理选项发生更改。通过对Namoi河的案例研究证明了这一点,结果表明,对MORE的扩展能够提供考虑到所有参数的同时变化的单个参数的敏感度信息。此外,当考虑到联合参数变化时发现的对单个参数的敏感性提高,这表明确保任何敏感性分析都应考虑这些变化的重要性。

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