Current failure thesis · for executives
Running the seeded plan…
Step 1 · Scenario composer
Compose the stress the plan must survive
Pick any combination (three or more is where it gets interesting). Stressors interact through named compound effects and through thresholds — governance load past 90%, change load past 100%, FastTrack automation past the 62% hard-case line — so a combination is not the sum of its parts. Step 2 measures that difference directly.
Compound effects — lit when both stressors are in the draft
Step 2 · Deterministic simulation
Outcome ranges
No run yet.
Step 3 · Failure-path replay
The most representative failure path
Step 4 · Mitigation portfolio
Spend scarce money and scarcer change capacity
Every selection re-runs the committed scenario with the same random numbers, so differences come from your choices, not noise. Money is a hard limit. Change capacity is soft: you can exceed it, and the model will make you pay in change saturation. Several mitigations slow value or create new risks — read the side effects.
Marginal analysis — what does each mitigation buy in this scenario?
Adds each unselected mitigation (or removes each selected one) on its own, 300 trials each, ignoring the budget limit. Positive = better.
Inspect · Causal failure map
Who and what makes failure grow
Stakeholders → initiatives and systems → failure mechanisms ← controls and mitigations. Red heat on a mechanism shows how often it took root in the last run. Select any node (click, or Tab + Enter) to see its causal paths; select a path to read it in plain language with the simulation's own evidence.
Inspect · Metric corruption lab
What your success metric teaches people to do
The chosen KPI is wired into the simulation: it changes hazards (gaming pressure), hides some mechanisms from detection, and drives what the board dashboard shows. Switch it and watch the gap between headline and outcome.
Compare all eight metrics in this scenario
Inspect · Adversary lenses
Six people who want this plan to change — for different reasons
Each critic reads the current portfolio, KPI and simulation through their own incentives. Each is partly right and has a blind spot. Their proposals conflict; applying one is reversible.
Where the critics collide (support / oppose by mitigation)
Inspect · Pre-mortem and dissent
It is month 18. The transformation failed. Why?
Step 5 · Decision package
Take the argument into the room
A local JSON file: scenario, assumptions, mitigations with budget and capacity use, outcome ranges, the representative failure path, dissent (including yours), metric choice and remaining unknowns. Nothing leaves this page.