Protocol
Draw decisions, uncertainties and value on one page
A spreadsheet can calculate a model whose logic nobody can see.
When it fits
- A consequential decision has many assumptions and it is unclear which uncertainty matters before which choice.
When to avoid it
- Do not treat a drawn arrow as proven causality; uncertain relationships remain assumptions to validate.
Why it matters
Sketch an influence diagram with three node types: decisions you control, uncertainties you do not, and outcomes or value you care about. Draw the meaningful dependencies and mark which uncertainty will be known before each decision. Use the picture to find missing assumptions, circular stories and information you cannot actually have in time.
Steps
- Every controllable choice is represented as a decision.
- Important uncertain events are separate from decisions.
- Outcome/value nodes show what the model ultimately judges.
- Arrows represent a specific dependence you can explain.
- Information available before each decision is distinguished from information learned later.
An example
For a migration cutover, map cutover timing, unknown defect rate, rollback feasibility, business interruption and final service impact.
Check your result
A reviewer can explain the decision logic without opening the calculation model.
Keep this limit in mind
- Do not treat a drawn arrow as proven causality; uncertain relationships remain assumptions to validate.
Connected ideas
AlternativeUse a decision tree when sequence changes what you know
Evidence and sources
Influence diagrams represent decisions, uncertainties, relationships and value in one graphical decision model and can also represent what information is available before a decision.
A diagram is only as good as the causal and informational assumptions encoded in it.
Fifty years of decision analysis in operational research: A review · Influence diagrams