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Sensitivity Analysis

Sensitivity analysis is the study of the stability of the rank order of alternatives given qualitative and quantitative variations introduced into model inputs. Unlike error propagation analysis, sensitivity analysis imposes perturbations on the inputs. Sensitivity analysis should also be distinguished from uncertainty analysis. When performing the latter we map assumptions onto inferences embracing multiple solutions rather than referring to any specific solution. On the other hand, sensitivity analysis is a backward-looking procedure that focuses on the robustness of a specific solution (Saisana, Saltelli, & Tarantola, 2005). Robustness is defined here as the minimal response of a model solution caused by changing input conditions. Consequently, the objective of sensitivity analysis is to strengthen the confidence in the obtained solution or, in the case of weak confidence, help to redefine the set of acceptable alternative solutions.

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