The Monte Carlo Simulation: Scope & Common Applications

Instructor: Saranya Ramachandran

Saranya has a Bachelors in Science focused on Electronics and Telecommunication and a Masters in Business Administration. She has 8 years of Project Management Experience and is PMP Certified.

This lesson will define Monte Carlo Simulations and briefly discuss their history and advantages, as well as how they're used to reduce the risk involved with complex decisions where outcomes are uncertain.

What Is a Monte Carlo Simulation?

Just like life, projects are uncertain. Before jumping into a project, it's wise to evaluate the risks. How do we analyze these risks? We can use tools that'll show us the outcome of our decisions. Monte Carlo Simulations are one such tool that's used to analyze risk and help us make better decisions.

A Brief History of the Monte Carlo Simulation

Random outcomes are most common in gambling, and there's a gambling spot in Monaco called Monte Carlo. The Monte Carlo Simulation is named after this gambling spot. A simulation is another word for imitating the actual process. In the Monte Carlo Simulation, we run through the various outcomes without actually going through a process or project. Stanislaw Ulam is the creator of the Monte Carlo Simulation, along with John von Neumann. Stanislaw Ulam's interest in the model arose when he wanted to predict his chance at winning in solitaire games.

How the Monte Carlo Simulation Works

The Monte Carlo Simulation is a quantitative model that predicts each outcome and what the likelihood of each outcome is; likelihood is termed as probability in quantitative analysis. For example, consider the decision of trying out a new restaurant. There's likely three outcomes for this decision: you might love the food, hate it, or just find it good enough to eat when you don't have other options. Each option in this case is said to be equally likely, and hence has a probability of one-third.

In every situation, we have the option to make a number of decisions. The decisions could be categorized as very conservative, very radical, or in between. The Monte Carlo Simulation considers each decision and all the possible outcomes for each decision. For example, there's a likelihood that a very conservative decision might result in a very undesirable outcome.

Advantages of the Monte Carlo Simulation

The major advantage of the Monte Carlo Simulation is that it helps to make decisions by analyzing various outcomes. When a project manager makes a decision using the Monte Carlo Simulation she or he needs to be able to communicate the reasons for her or his decisions to various stakeholders. With the Monte Carlo Simulation, a graph can be plotted with outcomes and their likelihood, which can be communicated easily. It also helps the decision maker to reconsider their inputs after looking at which input has the biggest negative impact on the result, as well as looking at the interdependence between inputs.

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