What does a small decision tree clarify?
Method: A decision tree separates choices from uncertain events and shows how each branch contributes to expected value. It makes assumptions inspectable before a decision is made.
Compare two choices across uncertain branches
- Write the two feasible choices.
- Define a favorable and adverse outcome for each.
- Estimate probabilities from the best available evidence.
- Compare expected value, downside, and reversibility together.
Read expected value beside the adverse branch
A pilot project may have a lower expected gain than a full rollout but a much smaller adverse branch. If the decision is reversible, that smaller experiment can still be the better next move.
Two outcomes are a deliberate simplification
Inspect reversibility and information value
A pilot may create evidence that improves a later decision, even when its immediate expected value is lower. A full commitment may remove that option. Record whether each branch is reversible and whether the outcome teaches something that changes future probabilities or available actions.
Vary the favorable probabilities and outcome values within plausible ranges. If a small change reverses the preferred option, the model is sensitive and the next step should improve the uncertain input rather than present the ranking as settled.
Boundary of the result
Two branches simplify reality. Add narrative notes for outcomes or dependencies that the model leaves out.
Continue with the method
Use the decision tree article and the expected value calculator and the assumption mapping worksheet to move from a quick result to a complete reasoning process.