Model uncertainty as probability distributions, not as single scenarios.
Purpose
Replaces point forecasts ("GDP will grow 2.3%") with distributions ("GDP between 1.4% and 3.1% with 80% confidence"), making uncertainty and assumption-sensitivity explicit.
Body
Named after the Monaco casino, Monte Carlo simulates thousands of possible evolutions by repeatedly sampling from input distributions. The output is not a number but a distribution — percentiles, VaR, expected shortfall. olivLaw uses Monte Carlo in Company VUCA (cash-flow projections) and GTM (product launches) with 10,000+ simulations per scenario.
Core concepts
- Probabilistic scenarios
- Uncertainty ranges
- Sensitivity analysis
- Scenario weighting
- Convergence (law of large numbers)