Modelling Dependent Uncertainties by Multivariate Gaussian Distributions in SMAA

We consider multicriteria decision-aid (MCDA) problems with multiple decision makers. In such problems, the uncertainty or inaccuracy of the criteria measurements can be represented as probability distributions. In many real-life problems the uncertainties may be dependent. However, it is often difficult to quantify these dependencies. Also most of the existing MCDA methods are unable to handle such dependency information. In this paper, we focus on MCDA problems where the criteria and their uncertainties are computed using a stochastic simulation model. The model is based on decision variables and stochastic parameters with given distributions. The simulation model determines for the criteria a joint probability distribution, which quantifies the uncertainties and their dependencies. We then use the SMAA-2 stochastic multicriteria acceptability analysis method for comparing the alternatives based on the criteria distributions. We demonstrate the use of the method in the context of a decision support problem of a retailer operating in the liberated European electricity market.