Monte Carlo Simulation

Today I discovered what is known as a Monte Carlo Simulation. Monte Carlo Simulation is a mathematical approach used to approximate the potential outcomes of uncertain events, thereby improving decision-making processes.

The mechanism entails building a model that assesses the likelihood of various outcomes in a system with unpredictability due to the intervention of random variables. Using random sampling, the technique generates a large number of potential outcomes and computes the average.

A three-step process is used to start a Monte Carlo Simulation:

1. Predictive Model Development: Define the dependent variable to be predicted and identify the independent variables.
2. Probability Distribution of Independent Variables: Using historical data, define a range of plausible values for independent variables and assign weights to them.
3. Iterative Simulation Runs: Run simulations iteratively by generating random values for independent variables until a representative sample with a large number of potential combinations is obtained.

   The frequency of sampling directly proportions to the precision of the sampling range and the accuracy of estimations. In essence, a greater number of samples results in a more refined sampling range, which improves estimation accuracy.