Uncertainty quantification for possibilistic/probabilistic simulation

A key requirement for using a simulation model to assess a highly complex system is the ability to characterize and quantify the uncertainty in the simulation results with respect to a typically immense set of possible combinations of values of the model´s input parameters. Some of these inputs may be sampled from a known or assumed probability distribution, but others are known only possibilistically. A biologically-inspired exploited search model is proposed to assess issues such as hazard, risk, and sensitivity analysis when possibilistic and probabilistic uncertainties interact. Finally, a method for holistic quantification of total uncertainty is presented.