Abstract
The analysis of models is a considerable problem when there is uncertainty in the model parameters and when the model parameters interact and determine the model output in a non-linear way. Usually, sensitivity analysis is applied to deal with the problem of uncertainty. However, if non-linearities and parameter interactions are strong, a sensitivity analysis is valid only in a small region around the chosen parameter combination. Moreover, the result of a sensitivity analysis will depend on the values assigned to the model parameters. A method is suggested in this paper which may be regarded as the `sensitivity analysis of a sensitivity analysis'. It determines how sensitively the output of a sensitivity analysis depends on the values assigned to the model parameters. The results of this analysis are used to identify those parameter combinations which encompass most of the variability in the output of a sensitivity analysis.
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