── ── Mental model
Decision Tree
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…
How it works
Step 1 — Root: Define the decision (options, timeline, decision-maker). Draw a square; each option is a branch.
Step 2 — Chance nodes: For each branch, identify uncertain events → draw circles. Branches at each circle must be MECE; probabilities must sum to 1.0.
Step 3 — Probabilities: Assign a number (0.0–1.0) + documented basis to every branch. Reject "50/50" without justification.
When to use it
- user says 'help me choose between two options with different risks', 'I need to map out what could happen if we go with X', 'we have a sequential decision — first we do A then depending on results we do B', 'what is the expected value of this investment given uncertain demand'
When not to use it
the decision is a one-shot choice with no sequential stages (use simple EV instead); probabilities cannot be estimated even roughly and uncertainty is too deep to quantify.
Worked example
Magee 1964 — Chemical Plant Investment (HBR)
John F. Magee (1926–2015) was a partner at Arthur D. Little and the first analyst to systematize decision tree methodology for business. His two 1964 Harvard Business Review articles introduced the framework to a generation of executives and consultants who had previously made multi-million-dollar investment decisions through intuition and scenario narratives.
Install this skill (free, MIT)
npx skills add deciqAI/knowledge-skills