When a computer model is used to inform a decision, it is important to investigate any uncertainty in the model and determine how that uncertainty may impact on the decision. In Probabilistic Sensitivity Analysis, model users can investigate how various uncertain model inputs contribute to the uncertainty in the model output. However, much of the literature only focusses on output uncertainty as measured by variance; the decision problem itself is often ignored, even though uncertainty as

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... ed by variance may not equate to uncertainty about the optimum decision. Consequently, traditional variance-based measures of input parameter importance may not correctly describe the importance of each input. We review a decision theoretic framework for conducting sensitivity analysis which addresses this problem. It is noted that computation of these decision-theoretic measures can be impractical for complex computer models, and so efficient computational tools using Gaussian process are also presented. An illustration is given in the field of medical decision making, and the Gaussian process approach is compared with conventional Monte Carlo sampling.