Best practice

When a quantitative decision model is worth building, and how to tell a good model from a bad one.

When to build a model

A model is worth it when

• The stakes are high
• The future is genuinely uncertain
• There are several real options
• The choice is hard to reverse
• People disagree about the numbers

A model is not worth it when

• The choice is cheap or easy to undo
• There is already a clear answer
• You have no information to base estimates on
• The decision is already made and the model would only justify it

The middle ground: when you are not sure, start with a rough model of a few boxes. If the answer is clear, stop. If it is close or the stakes are high, add detail where the tornado says it matters.

A good model versus a bad one

	Good	Bad
Framing	A clear decision and real alternatives, including the current approach	One option, or a foregone conclusion
Structure	Only what drives the outcome	Too sparse to explain anything, or sprawling with boxes that do not matter
Estimates	Honest ranges backed by notes and sources	Single guesses, or false precision
Dependencies	Linked inputs are correlated	Everything assumed independent
Win criteria	Stated before looking at results	Chosen afterwards to fit the answer
Results	Checked with sensitivity and a sanity check	Taken at face value
Revision	Updated as you learn	Built once and left
Evidence	Sources recorded, outside evidence widens rather than narrows	Numbers with no trail

Habits that help

1. Frame the decision in one sentence.
2. Always keep the current approach as the baseline.
3. Set the win rule before you run.
4. Write the low end of ranges first.
5. Research only the estimates that matter.
6. Re-run after every meaningful change.