A computational technique that estimates the distribution of possible outcomes for a decision by running thousands of simulated scenarios, each drawing input values from probability distributions rather than point estimates. Developed at Los Alamos during the Manhattan Project, Monte Carlo simulation excels at decisions where uncertainty compounds across many variables — financial projections, project timelines, supply-chain risk.
Its key insight is replacing a single best-guess forecast with a probability distribution, which forces the decision-maker to think in terms of ranges and confidence intervals rather than illusory point precision.