Monte Carlo Simulation.
A definition, in plain English — with the books that teach it.
What it means
Monte Carlo simulation applies thousands of randomized scenarios — each with different sequences of returns, inflation rates, and spending shocks — to estimate the probability that a financial plan succeeds or fails. Rather than projecting a single average return, it maps the full distribution of possible outcomes. Financial planners use it to stress-test retirement portfolios, while institutional investors use it for portfolio construction and risk management. The quality of results depends heavily on the assumptions fed into the model.
Example
A retiree runs 10,000 Monte Carlo scenarios with a $1.5 million portfolio, 4% annual withdrawals, and historical return/volatility assumptions. The simulation shows an 88% probability the portfolio lasts 30 years. Dropping withdrawals to 3.5% raises the success rate to 95%.