Monte Carlo Simulation Explained in 5 min

3-Second Blueprint (English)

1. Models uncertain inputs as probability distributions rather than fixed averages to simulate thousands of plausible future realities.
2. Relies on the Law of Large Numbers and Central Limit Theorem so repeated random sampling converges toward reliable outcome probabilities.
3. Maps full output curves across median, best-case, and tail-risk scenarios when multi-variable systems resist closed-form equations.