Principle
Stop asking for one forecast under deep uncertainty
Some decisions need robustness rather than a single best estimate.
When it fits
- When important assumptions cannot be assigned trustworthy probabilities or the environment can change structurally.
When to avoid it
- Scenarios and robust-decision tools organize uncertainty; they do not predict the future. Preserve governance, evidence and explicit review triggers.
Why it matters
When uncertainty is deep, define the decision and outcomes first, then examine multiple plausible conditions. Use point forecasts only where they are decision-useful and defensible.
An example
Instead of assuming one AI cost curve for a three-year platform choice, the team tests options under cheap, expensive and constrained-compute futures.
Check your result
The strategy does not depend on pretending one deeply uncertain forecast is known.
Keep this limit in mind
- Scenarios and robust-decision tools organize uncertainty; they do not predict the future. Preserve governance, evidence and explicit review triggers.
Connected ideas
Useful withIdentify uncertainties that can reverse the strategy
Evidence and sources
Supports
Robust Decision Making uses exploratory analysis to stress-test strategies over many plausible futures and seek strategies that perform acceptably across uncertainty rather than optimizing a single prediction.
Full RDM can require substantial modeling; lightweight cards should not claim equivalent rigor.
Robust Decision Making (RDM) · Abstract and method overview