Monte Carlo simulation

Another important tool at our disposal, that again intertwines with the previous methods discussed, is a Monte Carlo simulation. A Monte Carlo simulation is a computer-based method of analysis that was developed in 1940, and it uses statistical sampling to understand the probability (likelihood) of a situation (or outcome) occurring (thus it is a method of probabilistic analysis). It helps you analyse the impact of risks on your output (or projects). While you may not be using the Monte Carlo simulation in this course, it is worthwhile for you to understand the underlying foundations for this technique and how it can help you in your context.

The Monte Carlo simulation can be constructed under various probability distributions, and thus depending on which one you use, variables can have different outcomes. The method which you use will be dependent on whether you are trying to determine the probabilistic outcome of.

The following article has for more information on how a Monte Carlo simulation works. It outlines some of the typical probability distributions used, and when they may be used.

Read: Monte Carlo simulation [1]

Again, a Monte Carlo simulation is only as good as your estimates. It is important that you are aware of the importance of estimation when you interpret the outputs.

The following article expands a bit more on our understand of a Monte Carlo simulation, and provides examples of it in use:

Read: What is Monte Carlo simulation? [2]

References

1. Monte Carlo simulation [Internet]. Palisade. Available from: https://www.palisade.com/risk/monte_carlo_simulation.asp

2. What is Monte Carlo simulation? [PDF]. Riskamp. Available from: https://www.riskamp.com/files/RiskAMP%20-%20Monte%20Carlo%20Simulation.pdf