Monte Carlo is not a crystal ball. It is a decision framework that helps you evaluate strategy robustness under uncertainty and sequence risk.
Read distributions, not single outcomes
Focus on percentile ranges and failure probabilities rather than one expected path.
Robust strategies survive adverse paths, not just median outcomes.
Control for assumption drift
If you change multiple inputs at once, you lose causal clarity.
Run structured what-if experiments so each change has a measurable impact.
- Keep simulation count and seed policy consistent
- Adjust one variable per experiment
- Record before/after metrics and warnings
Turn output into action
The goal is not perfect prediction. It is disciplined decision-making under uncertainty.
Use output to pick safer sizing, identify bottleneck rules, and improve execution consistency.