What Robustness Simulation does
Perturbs the plan with odds drift, confidence noise, coverage slippage, and upset shocks to expose fragile assumptions.
Example: stress-test a narrow high-return plan
First simulate possible outcomes with the Monte Carlo Simulator, then run the robustness test to change the underlying assumptions. Inspect the fragility score and percentile bands to see how the modeled results spread when those assumptions are disturbed. Compare a revised setup with broader selections or different allocations if the original appears too sensitive.
How to use Robustness Simulation
Open Permutify and find Robustness Simulation in Simulation.
- Open Robustness Simulation.
- Run the stress test.
- Review the fragility score and percentile bands.
What to check before relying on the result
Stress results depend on the perturbation model and configured inputs. Percentile bands describe modeled outcomes, not guaranteed future limits. A stable simulation result is still distinct from a strategy validated on historical result records.
How does this differ from Monte Carlo Simulator?
Monte Carlo Simulator samples possible results to estimate performance. Robustness Simulation focuses on changing assumptions such as odds and confidence to expose sensitivity.
What should I inspect after the stress test?
Review the fragility score and percentile bands together. Consider whether the unfavorable modeled results fit your planning constraints before changing the setup.