No. 41 · Risk · Statistics and Probability
Monte Carlo Simulation
When odds are too tangled to calculate, play the future many times and count.
In the Almanac
“Ulam’s solitaire”, 1946 · “Financial Engines”, 1996
Both stories in full, three ways to use it and where it misleads.
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Questions
What is Monte Carlo Simulation?
Monte Carlo simulation uses random sampling to estimate outcomes that are too complex to calculate directly. You model the uncertain inputs, run the scenario thousands of times, and see how often each result occurs. The answer is a picture of probabilities rather than a single forecast.
How do you use Monte Carlo Simulation?
To use it, ask what happens across many possible versions of the future, not just the best guess. Check your savings odds, plan projects around realistic runs, and remember a simulation is only as good as its assumptions.
When does Monte Carlo Simulation matter?
Monte Carlo Simulation is one of the 32 mental models for risk, the situations where you ask: What could go wrong, and what will I do if it does? Within that, it belongs to “Thinking in odds”, alongside Expected Value, Fat-Tailed Distributions and Foxes vs Hedgehogs.
What is an example of Monte Carlo Simulation?
Two stories show it at work: Ulam’s solitaire (1946) and Financial Engines (1996). Both are told in full in The Mental Models Daily Almanac, which has two stories for each of its 365 models.
Which mental models are related to Monte Carlo Simulation?
Sensitivity Analysis, Garbage In Garbage Out and Scenario Analysis. Monte Carlo Simulation itself comes from statistics and probability.