── ── 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…
Run Decision Tree on a real problem
Bring something you're actually deciding — free, in the browser.
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
A Chipmaker's Leading-Edge Fab Investment Under AI Uncertainty (2024–2026)
Between 2024 and 2026, one of the largest irreversible capital bets in the economy was a semiconductor manufacturer's decision to commit to leading-edge fabrication capacity. A single advanced logic fab is a multi-year, multi-billion-dollar commitment made years before the demand that would fill it is confirmed. The AI wave made this sharper on both sides: the upside case (AI accelerators and their supply chain filling every wafer) and the downside case (a capacity glut if AI demand or monetization disappoints, echoing prior semiconductor boom-bust cycles).…
Install this skill (free, MIT)
npx skills add deciqAI/knowledge-skillsUseful? Star the repo — stars help other builders find it.
Related mental models
Cal Newport's framework (2016): deep work = undistracted, cognitively demanding activity that creates hard-to-replicate value; shallow work = logistical, responsive, easy-to-replicate work that fills modern calendars.
In Zero to One (2014, ch.
Most people confuse repetition with learning — they accumulate years of experience and plateau.
Design thinking — formalized by Tim Brown at IDEO and Stanford's d.school, grounded in Simon (1969) and Rittel's "wicked problems" — replaces assumption-driven decisions with evidence…