(Palisade: Ithaca, NY) -- When building simulation models with @RISK, consider the following best practices suggested by Palisade's director of training and consulting, Michael Rees, Ph.D.:

* A risk model may need to be built at an appropriate level of detail. A model which is too detailed will be more complex to add risk distributions to and will require more effort to capture the dependencies between the variables. In many practical cases, key dependencies will simply not be captured, and the result will have an excessively narrow range (for additive type models e.g., cost budgeting) or an excessively wide range (for subtractive models e.g., profit as the difference between uncertain revenues and costs).

* Consider whether or not to include event risks. Generically, a static model of a situation in which there are event risks (e.g., something adverse happening in 20 percent of cases in a reserve estimation model) would not include such a risk as a line item (because the most likely outcome is its nonoccurrence), whereas a risk model would.

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