Sensitivity analysis of decision making under dependent uncertainties using copulas

Many important decision and risk analysis problems are complicated by dependencies between input variables. In such cases, standard one-variable-at-a-time sensitivity analysis methods are typically eschewed in favor of fully probabilistic, or n-way, analysis techniques which simultaneously vary all n input variables and capture their interdependencies. Unfortunately, much of the intuition provided by one-way sensitivity analysis may not be available in fully probabilistic methods because it is difficult or impossible to isolate the marginal effects of the individual variables. In this paper, we present a dependence-adjusted approach for identifying and analyzing the impact of the input variables in a model through the use of probabilistic sensitivity analysis based on copulas. This approach provides insights about the influence of the input variables and the dependence relationships between the input variables. One contribution of this approach is that it facilitates assessment of the relative marginal influence of variables for the purpose of determining which variables should be modeled in applications where computational efficiency is a concern, such as in decision tree analysis of large-scale problems. In addition, we also investigate the sensitivity of a model to the magnitude of correlations in the inputs.