Comparing two methods for addressing uncertainty in risk assessments

The Monte Carlo method is a popular method for incorporating uncertainty re lative to parameter values in risk assessment modeling. But risk assessment models are often used as screening tools in situations where information i s typically sparse and imprecise. In this case, it is questionable whether true probabilities can be assigned to parameter estimates, or whether these estimates should be considered as simply possible. This paper examines the possibilistic approach of accounting for parameter value uncertainty, and provides a comparison with the Monte Carlo probabilistic approach. The comp arison illustrates the conservative nature of the possibilistic approach, w hich considers all possible combinations of parameter values, but does not transmit (through multiplication) the uncertainty of the parameter values o nto that of the calculated result. In the Monte Carlo calculation, on the o ther hand, scenarios that combine low probability parameter values have all the less chance of being randomly selected. If probabilities are arbitrari ly assigned to parameter estimates, without being substantiated by site-spe cific field data, possible combinations of parameter values (scenarios) wil l be eliminated from the analysis as a result of Monte Carlo averaging. Thi s could have a detrimental impact in an environmental context, when the mer e possibility that a scenario may occur can be an important element in the decision-making process.