Monte Carlo Simulation vs. Machine Learning

Simulation uses models constructed by experts to predict probabilities. Machine Learning builds its own models to predict future outcomes.

Monte Carlo (the place) is the iconic capital of gambling—an endeavor that relies exclusively on chance probabilities to determine winners and losers. Monte Carlo (the method) employs random inputs to models to make predictions on how a system will behave.

When subject matter experts create good Simulation models, they can be valuable in revealing probabilities in complex systems with large numbers of variables—such as predicting human behaviors in markets. “What if?” scenarios can be tested because individual data points or sets of data points can be manipulated to show their effects on the entirety.

Machine Learning builds its own models based on data sets of known outcomes. Predictions are done automatically by applying these models to new sets of data. This methodology is perfect for business analyses such as identifying customers who will churn or predicting customer lifetime value. No human input or modeling skill is required. “The cards call themselves,” as you might say for hands at the Baccarat table.

The take-away: Simulation excels where domain expertise can be captured to build accurate models to enable experimentation—even creating data inputs to see what happens. Machine Learning is best for fast, automatic predictions on new data based on observations of known outcomes. They are not mutually exclusive. In fact, Machine Learning can be handy to test and refine Simulation models.

Learn more about Squark’s AutoML.