Friday, November 9, 2018
Monte Carlo Simulations and Increasing Confidence in Estimates
Daily, C., and Solis, D. (2017). Monte Carlo Simulation: Assessing a Reasonable Degree of Certainty. The Value Examiner. May/June.
In this article, the authors demonstrate the value of conducting a Monte Carlo simulation in a business scenario. The authors present a company that will be conducting a damages analysis to claim lost profits. This scenario is suitable for a Monte Carlo simulation because of the number of inputs (i.e., loss period in years, lost revenue per year, save expenses as a percentage of revenues per year) and the variety of those inputs (i.e., 5-7 years, $50K-$150K in lost revenues, 20-30% in saved expense percentages). Prior to conducting the Monte Carlo simulation, the authors conduct a simple damages analysis by assuming the middle value of each range and calculating an estimate of the profits lost. The simple analysis produced an estimate of $450K profits lost (Figure 1).
Figure 1. Simple Analysis of Damages
After completing the simple analysis, the authors then run 10,000 separate Monte Carlo simulations with the @RISK software program. To do so, the authors modified their assumptions by defining probability distributions for each of the inputs. As a result, for each simulation run by @RISK, the inputs were randomly altered according to those probabilities. The results of the Monte Carlo simulations produced a mean lost profits of $450,056, which is nearly the same as the $450K produced by the simple analysis (Figure 2).
Figure 2. Monte Carlo Simulation Analysis.
Although the Monte Carlo simulations produced approximately the same estimate, the authors claim that the true value in the simulations is that they strengthen the degree of certainty in the estimate. Supported by statistical analysis, the estimate produced by Monte Carlo simulations includes confidence intervals for analysts to cite when expressing confidence in the estimate.
At first glance, the nearly identical estimates between a simple analysis and Monte Carlo simulations may prompt an analyst to question whether he/she should even bother with the simulations for this scenario. However, the authors make a great argument for the value of Monte Carlo simulations by stating that the simulations increase the degree of certainty in the estimate. This is especially valuable because analytic confidence may otherwise be determined by a degree of subjective opinion.
Although Monte Carlo simulations have its benefits, there are limitations. For example, Monte Carlo simulations are not suitable for intelligence tasks that are qualitative in nature since it requires quantitative data. Furthermore, analysts will need to be familiar with statistics and have access to a software program to run the simulations. Despite these limitations, Monte Carlo simulations have the capacity to improve confidence in an estimate and as such should be applied when possible.
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