A role for sensitivity analysis in presenting the results from MCDA studies to decision makers

Abstract

The aim of sensitivity analysis (SA) is to ascertain how much the uncertainty in the output of a model is influenced by the uncertainty in its input factors. An SA can be performed using different methods, which are classified according to various criteria. One possible classification is that in which global and local approaches are identified. This paper strengthens the role of global SA methods and suggests their use in the context of MCDA. Useful applications of global SA already exist in a variety of fields where numerical models are considered, e.g. in economics, engineering and chemistry. Global SA is quite different in its formulation and application from local SA, which is seen more frequently in the literature. The global sensitivity indices adopted are derived from a variance decomposition scheme: they can be estimated by alternative computational strategies, such as the extended FAST or the method proposed by Sobol'. A truly global SA is capable of apportioning the output uncertainty according to any subgroup of input factors. Hence, the output uncertainty of an MCDA can, for instance, be decomposed into a part due to uncertain model inputs and a part due to poorly defined (or variable) weights attached to the criteria. This information could be useful to the decision maker (DM) since it explains synthetically how much the assessment of an MCDA study is biased by the assessor judgements. Alternative regrouping of the uncertain input elements might shed light on other features of the problem addressed by the DM.

A decision analytic model to guide early‐stage government regulatory action: Applications for synthetic biology

“…However, the secure labs only (0.64), IRB pre‐research (0.61), and ERB post‐research (0.59) alternatives also performed well. We will further discuss quantitative reasons why the alternatives score as they do, and perform sensitivity analysis to determine which conditions the existing optimality may shift (Saltelli et al ). Such an assessment allows for an understanding of how resilient and dependable the selection of ERB pre‐research is as the highest scoring governance option for SB cyberplasm research and development (Saltelli et al ; Stewart ).…”

“…Lastly, we sought to further test the magnitude of the difference in scores by conducting sensitivity analysis on the primary criteria's weights (an example of the economic criteria is shown in Figure ) (Saltelli et al ; Linkov et al , ). Specifically, we reviewed the impact of increasing or reducing the degree of importance assigned to a given criterion, with the difference removed or added to the other five criteria distributed equally.…”

“…Specifically, MCDA integrates qualitative and quantitative information alike, allowing experts to voice their beliefs and opinions on a subject with limited field data or formal scientific agreement (Linkov & Moberg ; Linkov et al ). Further, MCDA tools can be used to review any disagreements or uncertainties signaled by subject experts through the use of sensitivity analysis, making MCDA all the more helpful for regulators that have to address shifting political, economic, social, and environmental factors as more information about specific products becomes available (Saltelli et al ; Linkov et al ). Given these factors, MCDA serves as an appropriate method to analyze information derived from the Delphi process and rank governance options for cyberplasm based upon relevant decision criteria.…”

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Using the PROSA Method in Offshore Wind Farm Location Problems

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