Quality assurance for Monte Carlo risk assessment

Abstract

Three major problems inhibit the routine use of Monte Carlo methods in risk and uncertainty analyses: correlations and dependencies are often ignored; input distributions are usually not available; and mathematical structure of the model is questionable. Most practitioners acknowledge the limitations induced by these problems, yet rarely employ sensitivity studies or other methods to assess their consequences. The paper reviews several computational methods that can be used to check a risk assessment for the presence of certain kinds of fundamental modeling mistakes, and to assess the possible error that could arise when variables are incorrectly assumed to be independent or when input distributions are incompletely specified.