EVALUATION OF SOFTWARE FOR PROPAGATING UNCERTAINTY THROUGH RISK ASSESSMENT MODELS

Quantitative uncertainty analysis has become a common component of ris k assessments. In risk assessment models, the most robust method for p ropagating uncertainty is Monte Carlo simulation. Many software packag es available today offer Monte Carlo capabilities while requiring mini mal learningtime, computational time, and/or computer memory. This pap er presents an evaluation of six software packages in the context of r isk assessment: Crystal Ball, @Risk Analytica, Stella II; PRISM, and S usa-PC. Crystal Ball and @Risk are spreadsheet-based programs; Analyti ca, and Stella II are multi-level, influence-diagram based programs de signed for the construction of complex models; PRISM and Susa-PC are b oth public-domain programs designed for incorporating uncertainty and sensitivity into any model written in Fortran. Each software package w as evaluated on the basis of five criteria, with each criterion having several sub-criteria. A 'User Preferences Table' was also developed f or an additional comparison of the software packages. The evaluations were based on nine weeks of experimentation with the software packages including use of the associated user manuals and test of the software through the use of example problems. The results of these evaluations indicate that Stella II has the most extensive modeling capabilities and can handle linear differential equations. Crystal Ball has the bes t input scheme for entering uncertain parameters and the best referenc e materials. @Risk offers a slightly better standard output scheme and requires a little less learning time. Susa-PC has the most options fo r detailed statistical analysis of the results, such as multiple optio ns for a sensitivity analysis and sophisticated options for inputting correlations. Analytica is a versatile, menu-and graphics-driven packa ge, while PRISM is a more specialized and less user friendly program, When choosing between software packages for uncertainty and sensitivit y analysis, the choice largely depends on the specifics of the problem being modeled. However, for risk assessment problems that can be impl emented on a spreadsheet, Crystal Ball is recommended because it offer s the best input options, a good output scheme, adequate uncertainty a nd sensitivity analysis, superior reference materials, and an intuitiv e spreadsheet basis while requiring very little memory.