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Monte Carlo technique for understanding the range of financial outcomes

Ellis Mirsky, [] describes how he uses Excel and a program called Crystal Ball (from Decisioneering, Inc.) to run Monte Carlo simulations. [Article at]. This post skims the surface of a complex topic.

A Monte Carlo simulation runs thousands of iterations for each of many independent and dependent variables identified for a modeled scenario. A law department has some control over independent variables, such as choice of law firm and amount paid in settlement. Dependent variables are ones that the department would ultimately like to control. Dependent variables, such as total costs of resolution, stem from the facts of the case and the values of the independent variables.

When you fill in data for the variables in your model, the Monte Carlo software produces distributions of outcomes that tend to converge to a most likely outcome. Statistics make more sense out of the results. Mirsky has used the Monte Carlo technique to help companies set litigation reserves, evaluate the most cost effective way to handle lawsuits, and assign lawyers to cases.

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