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 (EV scaffold + bet sizing), probabilistic-thinking (calibration per node), inversion (rollback = working outcomes backward), mece (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
• Sizing a big irreversible bet under AI uncertainty — build vs. buy vs. wait on AI capex, a fab investment, or committing while AI adoption / AI valuations are unproven

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)