Depending upon the number of factors which are considered "uncertain" and the range of possible values specified for each of them, a Monte Carlo simulation could involve thousands, or even tens of thousands, of recalculations. It then calculates the results numerous times, each time using a different set of random values from the probability functions. It does this by building models of possible results using a probability distribution that is, by substituting a range of values for any specific factor chosen by the decision maker. How Monte Carlo Simulation WorksĪ Monte Carlo simulation performs a risk analysis of any chosen decision factor that has inherent uncertainty. Since then, it has been used in many applications in widely diverse fields such as finance, project management, energy, manufacturing, engineering, research and development, insurance, oil & gas, transportation, and the environment. In other words, it will show the potential consequence of both the most aggressive and the most conservative decision as well as providing the corresponding data for any "middle of the road" decision between the two extremes.Įarly use of Monte Carlo simulation was made by scientists of the Manhattan Project - the development of the first atomic weapons during WWII - to help predict neutron penetration when they were investigating radiation shielding. A Monte Carlo simulation will provide the user with a range of possible outcomes and the probability of occurrence for each choice of action. Monte Carlo simulation is a computerized mathematical technique that enables risk to be accounted for in quantitative analysis and decision making. Monte Carlo simulation allows generation of all the possible outcomes of the decision before it is made, thus allowing the assessment of the impact of risk and allowing for better decision making in the face of uncertainty. Even though data and information upon which to make the decision might easily be available from multiple sources, the future cannot accurately be predicted and the ultimate outcome of the decision is still an unknown quantity. However, many of those decisions are made in the face of uncertainty, ambiguity, and variability. Risk analysis is an important part of almost every decision. Monte Carlo Simulation, sometimes referred to as the Monte Carlo method, is a computerized mathematical technique that allows risk to be accounted for in quantitative analysis and decision making.
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