decision-treelisted

# Decision Tree ## Overview A **decision tree** maps a multi-stage decision: decision nodes (squares) for choices you control, chance nodes (circles) for outcomes you don't, probabilities on every branch, payoffs at the leaves — then rollback right-to-left to get expected value at the root. First systematized by John F. Magee (HBR, 1964); formalized by Howard Raiffa (1968). Its biggest value: converting "I feel we should expand" into "what probability do you assign to high demand?" — making every assumption explicit and contestable. Composes with [`expected-value-and-kelly`](../expected-value-and-kelly/SKILL.md) (EV scaffold + bet sizing), [`probabilistic-thinking`](../probabilistic-thinking/SKILL.md) (calibration per node), [`inversion`](../inversion/SKILL.md) (rollback = working outcomes backward), [`mece`](../mece/SKILL.md) (branches must be MECE so probabilities sum to 1.0). ## When to Use - Decision has sequential stages (decide → learn → decide again) - Outcomes uncertain; probabilities can be estimated (even roughly) - Payoffs quantifiable (NPV, revenue, cost, lives saved) - Multiple stakeholders need a shared visual model to align on assumptions **Not when:** one-shot choice with no stages; probabilities unestimable; payoffs purely qualitative; branch set too large (use scenario planning instead). ## Coaching Novices (Adaptive Front Door) - **Engine mode:** user has a concrete multi-stage decision → run The Process directly. - **Coach mode:** user is unfamilia