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Scenario planning
scenario-planning 128 mixed-layer hits
A structured way to think about the future when prediction is impossible. Build three to four distinct, internally-coherent stories of how the world could unfold, and stress-test strategy against each. The output is a set of named worlds the team can argue with — not a probability distribution.
92 docs 127 slides/ranges 25% confidence
- When to use
- Time horizon ≥12 months · ≥2 high-impact uncertainties that cannot be expressed as probability distributions · top two uncertainties roughly independent · team commits to revisit every 12–18 months. For shorter horizons use sensitivity analysis; for quantifiable risk use Monte Carlo; for one-dominant-uncertainty use cascade scenarios.
- Why it works
- Solves the two failure modes of forecasting at once: being precisely wrong (point estimates anyone can falsify) and uselessly hedged (ranges so wide they cannot guide action). Scenarios commit to a small set of plausible worlds and crystallise strategy into no-regret moves, sequenced bets, and lose-everywhere moves to drop. Names + early signals + wind-vane triggers turn the exercise into a usable contingency plan.
- Purpose
- Force narrative discipline on multi-year decisions where single-point forecasts fail. Translate structural uncertainty into a small set of decision frames the boardroom can act on by name.
- Anti-pattern
- Best/base/worst case (three points on one axis, not four scenarios) · probability-weighted average of scenarios (collapses to expected value) · Goldilocks-spaced scenarios that rank good-to-bad · scenarios named "Scenario A/B/C" instead of world-shape names · 50-row Excel labelled as a scenario (model, not story) · scenarios without measurable early signals.
tablecalloutquote scenariouncertaintyno-regretdriving forcescritical uncertainties2x2 matrixplausible futureearly signalscontingencymatrix
Observed evidence keyword-coverage: no-regret, uncertainty
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