MONTE-CARLO MODELING WITH UNCERTAIN PROBABILITY DENSITY-FUNCTIONS

Monte Carlo modelling is a powerful mathematical technique that offers many advantages compared to traditional point estimate methods for ch aracterizing the inherent variability in exposure to environmental che micals among different members of a population. However, Monte Carlo a nalyses of variability are often limited by uncertainty (lack of knowl edge) about the true distribution of key exposure and risk parameters. Because of this uncertainty, it is not appropriate to select only one value (either a ''best estimate'' or, even worse, an intentionally co nservative value) for each uncertain parameter, because the result of a simulation employing such fixed point estimates is only one of many possible results that could be true. The solution to this problem is t o run repeated Monte Carlo simulations, using different combinations o f the uncertain parameters as inputs. The degree of variation between the output of different simulations then reveals how certain (or how u ncertain) any particular estimate (e.g., mean, 95th percentile) of exp osure or risk may be. This type of information maximizes the opportuni ty for risk managers to make informed decisions.