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Sensitivity analysis. Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be divided and allocated to different sources of uncertainty in its inputs. [1][2] This involves estimating sensitivity indices that quantify the influence of an input or group of inputs on ...
Tornado diagrams are useful for deterministic sensitivity analysis – comparing the relative importance of variables. For each variable/uncertainty considered, one needs estimates for what the low, base, and high outcomes would be. The sensitive variable is modeled as having an uncertain value while all other variables are held at baseline ...
Elementary effects method. Published in 1991 by Max Morris [1] the elementary effects (EE) method[2] is one of the most used [3][4][5][6] screening methods in sensitivity analysis. EE is applied to identify non-influential inputs for a computationally costly mathematical model or for a model with a large number of inputs, where the costs of ...
See Corporate finance: Quantifying uncertainty. Additionally to the general motivations listed above, sensitivity analysis can help in a variety of other circumstances specific to business: To identify critical assumptions or compare alternative model structures. To guide future data collections. To optimize the tolerance of manufactured parts ...
A sensitivity analysis may reveal surprising insights in multi-criteria decision making (MCDM) studies aimed to select the best alternative among a number of competing alternatives. This is an important task in decision making. In such a setting each alternative is described in terms of a set of evaluative criteria.
Variance-based sensitivity analysis (often referred to as the Sobol’ method or Sobol’ indices, after Ilya M. Sobol’) is a form of global sensitivity analysis. [1] [2] Working within a probabilistic framework, it decomposes the variance of the output of the model or system into fractions which can be attributed to inputs or sets of inputs.
Morris method. In applied statistics, the Morris method for global sensitivity analysis is a so-called one-factor-at-a-time method, meaning that in each run only one input parameter is given a new value. It facilitates a global sensitivity analysis by making a number of local changes at different points of the possible range of input values.
Sensitivity auditing is an extension of sensitivity analysis for use in policy-relevant modelling studies. Its use is recommended - i.a. in the European Commission Impact assessment guidelines and by the European Science Academies - when a sensitivity analysis (SA) of a model-based study is meant to demonstrate the robustness of the evidence provided by the model in the context whereby the ...