The uncertainty analysis consists in evaluating how the variability inherent to the assumptions of a model, influences its outcome.

Monte Carlo Simulation is one of the most common and contrasted techniques in this type of analysis, and consists of performing a large number of evaluations of the model by choosing, each time, random values from the PDFs that represent the values each variable can take.

The most basic form of uncertainty analysis is the sensitivity analysis, which relates the variation of a variable's values, with the result for a given objective. Our methods, besides, combine variables to build scenarios, identify those in which scenarios strategies have a resilient or vulnerable behaviour, and determine the risks and opportunities that each strategy presents for the multiple objectives analysed, both individually and globally considered.