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Glossary
Decision tree
A decision tree is a structured map of sequential choices (decision nodes), uncertain events (chance nodes), and terminal payoffs. It supports expected-value calculation along paths and makes contingencies explicit — what you will do if intermediate outcomes go different ways.
Why it matters
Complex decisions hide nested if-then logic. Trees force clarification of order: what must be chosen now versus what can wait for information. They pair naturally with expected value when probabilities on chance nodes are credible, and they expose where information-gathering or real options change the tree’s value.
Trees are not scenario planning. Scenarios describe alternative worlds; trees describe sequences of choices and chance events inside an analysis. Oversized trees become theater — the goal is decision-relevant structure, not exhaustive branches.
Example (enterprise): A build-vs-buy tree separates the immediate choice from later scale-up nodes contingent on pilot metrics, enabling an EV comparison that includes the value of waiting for pilot evidence.
Mechanisms
- Order decisions and uncertainties — what must be fixed now versus after learning.
- Assign payoffs (and probabilities when defensible) to terminal and intermediate outcomes.
- Roll back the tree — choose at each decision node the branch with the best expected value or chosen criterion.
Boundaries
- Decision tree ≠ scenario planning; different objects (sequences vs contextual futures).
- Decision tree ≠ automatic optimality; bad probabilities or missing branches mislead.
- Huge ornamental trees do not improve decisions if leadership cannot act on the structure.
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