Monte Carlo simulation solves complex probabilistic systems by generating large ensembles of pseudo-random samples drawn from underlying probability distributions. By virtue of the Law of Large Numbers and Central Limit Theorem, the empirical sample mean converges asymptotically to the true mathematical expectation, with numerical uncertainty attenuating inversely with the square root of sample size N.