What is the purpose of Monte Carlo simulation in risk analysis?

• A. To model uncertainty by generating many possible outcomes and estimating distribution of results.
• B. To predict a single most likely outcome with complete certainty.
• C. To eliminate all uncertainty from the analysis.
• D. To replace qualitative risk assessments entirely.

Answer: (A)

Monte Carlo simulation in risk analysis models uncertainty by generating many possible outcomes and estimating the distribution of results. It does this by assigning probability distributions to uncertain inputs (like costs, durations, demand, etc.), then running a large number of trials with random samples from those distributions. The collection of results builds a probability distribution for the outcome of interest, letting you quantify risk—how likely you are to exceed a threshold, the expected value, and the potential for extreme losses or gains. This approach helps compare options, identify which inputs drive risk through sensitivity analysis, and communicate uncertainty to stakeholders. It does not predict a single most likely outcome with complete certainty, nor does it eliminate uncertainty or replace qualitative risk assessments—the simulations describe what could happen and how likely it is, while qualitative judgment adds context that numbers alone can’t provide.