Build plans with slack for outcomes outside your model
Reserve capacity for events that are not in your risk model — because the most damaging events usually aren’t.
Why it works
Standard risk management lists known risks and allocates contingency to them. But Taleb’s argument — consistent with historical evidence on financial crises, pandemics, and technological disruption — is that the events that cause the most damage are typically outside the pre-crisis model entirely. Building in general slack (financial reserves, timeline buffers, optionality) that can be deployed for unknown unknowns rather than only pre-specified risks is the practical correction.
How to do it
- After listing your known risks and contingencies, add a separate budget for "unknown unknown" events.
- Size this as a percentage of total capacity, not as a response to any specific scenario.
- Resist the pressure to specify what the slack is for — the point is that it can’t be specified in advance.
Evidence
Consistent with planning fallacy research and with post-crisis analysis of failures across finance, medicine, and infrastructure: models that only account for known risks systematically underestimate total risk. Taleb documents this historically in The Black Swan. (observational)
The scale of slack needed depends heavily on domain volatility; there is no universal percentage. This is a direction, not a formula.
Common mistake
Treating the unknown-unknown budget as a waste when it isn’t used for several years and eliminating it to optimize efficiency — exactly when it is most needed.
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More practices for The Ludic Fallacy: When You Mistake Real Life for a Game
- Check whether the rules of your domain are actually stable
Before applying any probability model, ask whether the rules governing outcomes could change mid-game.
- Stress test plans against outcomes beyond the historical range
Ask how your plan holds up if the worst outcome is twice as bad as any historically observed case.
- Prefer positions with optionality over positions with precision
In uncertain environments, prioritize options to pivot over optimized fixed positions.
- Beware false precision in forecasts and models
Treat any precise probability or quantitative forecast with explicit suspicion about whether the model fits the domain.
- Question whether the category you’re reasoning from actually fits
Before applying a model or framework, verify that the category it was built on genuinely matches your situation.
Related concepts
- The Narrative Fallacy: Why We Can’t Stop Making Stories
How causal stories distort hindsight, forecast, and learning — and how to reduce their pull
- Thinking, Fast and Slow, Made Usable
Two systems, the biases they create, and when to slow down
- Base-Rate Neglect: Why We Ignore the Odds
How to let prior probabilities do their real work in your decisions