Monte Carlo Simulations well explained, and the ability to do a sensitivity analysis

A clear explanation of the statistical model, known as the Monte Carlo simulation appears in Womble Carlyle’s FocusExtra, 2Q2009 at 1, by Bill Turner. The newsletter explains the technique in the context of estimating the cost of a lawsuit through trial. Essentially, for four stages of a litigation, the law firm and the law department — or just the law department — need to estimate the cost for each stage, as well as a low-cost estimate and a high-cost estimate. Software can then run multiple iterations where it uses data from that table to prepare a bell shaped curve of likely outcomes (See my post of May 15, 2005: Monte Carlo simulations as computational models.).

Because the outcome curve meets the requirements of a normal, Gaussian distribution, the law department can calculate confidence intervals for any given total cost. That means you can say, for example, with 80 percent confidence that the total cost will be $1.2 million.

A law department can also use the data and software for a sensitivity analysis. A sensitivity analysis tells which factors within the model create how much of the variation, the factors here being the four stages of litigation. This analysis is similar to what a multiple regression analysis can calculate.

The piece suggests that a law department should do this calculation on its own and then compare a proposed fee by the law firm to the likelihood of that fee being the total cost according to the Monte Carlo simulation.

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