Monte Carlo simulation models an uncertain outcome by running many randomized trials, each drawing input values from their probability distributions, to produce a distribution of possible results rather than a single estimate. It reveals both the range of outcomes and how likely each is.
When a forecast depends on many uncertain inputs, adoption rates, pricing, cost, and timing, combining their most-likely values produces a single number that is almost never what happens, because the interactions and compounded probabilities are impossible to hold in your head. Monte Carlo simulation runs the model thousands of times, each trial sampling every uncertain input from its range, and aggregates the results into a probability-weighted forecast. The output is a spread of outcomes with confidence bands, plus the raw material for sensitivity analysis. For how BRI carries uncertainty through its models, see the Modeling Uncertainty page at /supporting/uncertainty. Related Terminology Index entries: Uncertainty Assumptions; Sensitivity Analysis; Portfolio.
The method was developed by Stanislaw Ulam and John von Neumann at Los Alamos in the 1940s and named by Nicholas Metropolis; Metropolis and Ulam's 1949 paper “The Monte Carlo Method” is the canonical reference. Its translation into everyday business decision-making is argued in Sam Savage's The Flaw of Averages (2009). BRI's contribution is to embed it directly in strategy modeling rather than a specialist's spreadsheet: Growth Forge® Software runs Monte Carlo simulation across the ranged assumptions in the Market Sizing and Portfolio Modeling tools, so a practitioner without a statistics background gets a defensible forecast range and a ranked list of the assumptions that move it most.
