How does Monte Carlo simulation assist in risk analysis?

How does Monte Carlo simulation assist in risk analysis?

• A. It deterministically predicts the single most likely outcome
• B. It uses random sampling and probability distributions to model uncertainty and estimate the range and likelihood of project outcomes
• C. It ignores probability to simplify
• D. It requires only qualitative judgments

Answer: (B)

Monte Carlo simulation in risk analysis uses random sampling from defined probability distributions for uncertain inputs (like cost, duration, or productivity) and propagates those through the project model. By running a large number of iterations, it builds a distribution of possible outcomes instead of a single point estimate. This lets you quantify risk: you can estimate the probability of finishing on time or within budget, see the likely range of results, and compute confidence intervals. It also reveals which inputs drive the most variability, guiding where to focus risk mitigation. Since it relies on probability and quantitative inputs, it embraces uncertainty rather than ignoring it, providing a spectrum of possibilities and their likelihoods rather than one fixed forecast.

Monte Carlo simulation in risk analysis uses random sampling from defined probability distributions for uncertain inputs (like cost, duration, or productivity) and propagates those through the project model. By running a large number of iterations, it builds a distribution of possible outcomes instead of a single point estimate. This lets you quantify risk: you can estimate the probability of finishing on time or within budget, see the likely range of results, and compute confidence intervals. It also reveals which inputs drive the most variability, guiding where to focus risk mitigation. Since it relies on probability and quantitative inputs, it embraces uncertainty rather than ignoring it, providing a spectrum of possibilities and their likelihoods rather than one fixed forecast.