Sensitivity Analysis Versus Uncertainty Analysis: When to Use What?

Abstract

Decision makers and other users of models are interested in model validity. From their viewpoint the important model inputs should be split into two groups, namely inputs that are under the decision makers' control versus (environmental) inputs that are not controllable. Specifically, users want to ask 'what if' questions about global (not local) sensitivities: what happens if controllable inputs are changed (scenario analysis), what if model parameters and structure change? Among the techniques to answer these questions are statistical design of experiments (such as fractional factorial designs) and regression analysis. These techniques may show that some non-controllable inputs of the model are important; yet these inputs may not be known precisely. Then risk or uncertainty analysis becomes relevant. Its techniques are Monte Carlo sampling, including variance reduction techniques (such as Latin hypercube sampling), possibly combined with regression analysis. Controllable inputs can be optimized through Response Surface Methodology (RSM).