Core Methodology
There are a large number of approaches to performing a sensitivity analysis, many of which have been developed to address one or more of the constraints discussed above. They are also distinguished by the type of sensitivity measure, be it based on (for example) variance decompositions, partial derivatives or elementary effects. In general, however, most procedures adhere to the following outline:
- Quantify the uncertainty in each input (e.g. ranges, probability distributions). Note that this can be difficult and many methods exist to elicit uncertainty distributions from subjective data .
- Identify the model output to be analysed (the target of interest should ideally have a direct relation to the problem tackled by the model).
- Run the model a number of times using some design of experiments, dictated by the method of choice and the input uncertainty.
- Using the resulting model outputs, calculate the sensitivity measures of interest.
In some cases this procedure will be repeated, for example in high-dimensional problems where the user has to screen out unimportant variables before performing a full sensitivity analysis.
This section discusses various types of "core methods", distinguished by the various sensitivity measures that are calculated (note that some of these categories "overlap" somewhat). The following section focuses on alternative ways of obtaining these measures, under the constraints of the problem.
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