Monte Carlo Methods, Decoded

Solving complex problems with simulations

The name “Monte Carlo” for the Monte Carlo methods has an origin that ties back to the famous Monte Carlo Casino located in Monaco. This name was not chosen because of any direct association with the mathematical principles behind these methods, but rather for its metaphorical connection to randomness and chance, which are central elements in both gambling and Monte Carlo simulations. In this post, we will discuss this technique and show code examples related to project management, approximating irregular areas, and gaming.

Real-world systems and processes often involve uncertain parameters and variables. With Monte Carlo methods you can explicitly model these uncertainties. Businesses can make better informed decisions by understanding the probability and impact of different risks. Besides decision support, you can use it for enhancing predictive models and/or communication.

Jar with marbles. Image created with Dall·E by the author.

The Basics

Imagine you have a big, mysterious jar full of different-colored marbles. There is one problem: you can’t see inside it to count how many of each color there are. You want to know which color you’re most likely to pick if you reach in the jar without looking.