Protocol
Sweep one uncertain assumption across a plausible range
A single estimate hides how much the answer depends on being lucky about the assumption.
When it fits
- A model output looks precise but depends on an uncertain input.
When to avoid it
- One-at-a-time sensitivity can miss interactions; use broader scenario or probabilistic analysis when assumptions move together.
Why it matters
Choose one material assumption and vary it across a defensible low-to-high range while holding the rest of the model fixed. Record how the value of each option changes. Repeat for the assumptions that matter most. This is a diagnostic of model sensitivity, not a forecast that every input will move independently.
Steps
- You know whether the decision remains the same across the plausible range or depends strongly on that assumption.
An example
Vary the expected migration effort from 10 to 30 days and see whether the preferred automation approach changes.
Check your result
You know whether the decision remains the same across the plausible range or depends strongly on that assumption.
Keep this limit in mind
- One-at-a-time sensitivity can miss interactions; use broader scenario or probabilistic analysis when assumptions move together.
Connected ideas
Useful withFind the assumption value where the choice flips
Evidence and sources
Decision analysis uses sensitivity analysis to examine how changes in uncertain inputs or assumptions affect model results.
Changing one input at a time can miss interactions among assumptions.
Fifty years of decision analysis in operational research: A review · Sensitivity analysis