Decision-theoretic sensitivity analysis for complex computer models

When using a computer model 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 focuses only on output uncertainty as measured by variance; the decision problem itself often is ignored, even though uncertainty as measured 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 that addresses this problem. Because computation of these decision-theoretic measures can be impractical for complex computer models, we provide efficient computational tools using Gaussian processes. We give an illustration in the field of medical decision making, and compare the Gaussian process approach with conventional Monte Carlo sampling.