Heuristic
Prefer robust-enough over optimal-for-one-forecast
Fragile optimality can be worse than bounded adequacy.
When it fits
- When one option has the highest modeled payoff in the base case but fails badly in nearby plausible conditions.
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
Define minimum acceptable outcomes and compare how consistently options stay above them across plausible futures. Keep upside visible, but do not hide catastrophic sensitivity.
An example
A slightly lower-return architecture is preferred because it remains supportable across three data-volume regimes while the base-case winner collapses in two.
Check your result
The choice explicitly trades peak modeled performance against robustness.
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 withDesign signposts that tell you when the world changed
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