deciqAI 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)
- Engine mode: user has a concrete multi-stage decision → run The Process directly.
- Coach mode: user is unfamiliar or has no concrete case → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- One-line: a decision tree converts "I feel" into "what probability do you assign?" — making assumptions explicit so they can be argued about.
- Check fit. Sequential stages? Uncertain outcomes? Quantifiable payoffs? If yes to all three, a tree applies.
- Elicit their real case. What's the initial choice? What uncertain outcomes follow? What payoffs result?
[WAIT — do not advance until user responds]
- Run The Process one step at a time with their input — draw structure, assign probabilities, assign payoffs, roll back.
[WAIT — do not advance until user responds]
- Close by naming the insight: the threshold at which the recommendation flips, and whether to gather more data.
[WAIT — do not advance until user responds]
The Process
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.
Step 4 — Payoffs: Assign consistent-unit payoffs (NPV, revenue, etc.) to every terminal leaf.
Step 5 — Rollback: Right to left — EV at each circle = Σ(p × value). At each square, keep highest EV branch; mark losers //.
Step 6 — Sensitivity + stop-rule: Find the probability threshold where the optimal choice switches. Compute EVPI = EV(perfect info) − EV(best decision now). If EVPI < cost of data: decide now. If EVPI > cost: gather data first. Stop refining when the leading option's EV advantage exceeds the value of further analysis.
Output Template
Decision Tree: <topic>
Options: A / B | Timeline:
Tree: [node-by-node description]
Probabilities: Node | Branch | p | Basis
Payoffs: Path | Value | Unit
Rollback: Option A EV= / Option B EV= / Optimal=
Sensitivity: flips when p([key branch]) > [threshold] | EVPI=
Recommendation: [option] — holds if [condition]; flips if [condition]
→ Method in Action: Magee 1964 — Chemical Plant Investment (HBR) · Eisenhower's D-Day Weather Decision → 2026 lens: A Chipmaker's Leading-Edge Fab Investment Under AI Uncertainty (2024–2026)
Pack: Decision Tree by Domain
| Domain | Root Decision | Key Uncertainty | Payoff | Watch For |
|---|---|---|---|---|
| Capital investment | Large vs. small plant | Demand scenarios | NPV | Overconfident demand p |
| R&D portfolio | Fund vs. kill | Technical success; adoption | Revenue × p | Ignoring base-rate failure |
| Litigation | Settle vs. litigate | Win/lose; damages | Expected settlement | Anchoring on best case |
| Product launch | Now vs. delay | Market reception; competitor | Revenue per scenario | Missing competitor-first branch |
| M&A | Acquire vs. pass | Integration; synergy | Post-acquisition EV | Paying for performance peak |
Applying It Well
- Draw before calculating — structure surfaces hidden assumptions
- Assign probabilities before revealing your preferred option
- Run sensitivity before concluding; find the switchover threshold and EVPI
- Audit missing branches explicitly: "what did we leave out?"
→ Primary sources: references/sources.md
Common Rationalizations
[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.
| Rationalization (Fake Move) | Reality |
|---|---|
| [D] "This is strategic — we don't need numbers." | Without numbers the tree is just a picture. Force strategic disagreements to become numerical ones. |
| [D] "We can't estimate probabilities." | Even rough estimates beat implicit zero/one assumptions. Every un-numbered branch already has an implicit probability. |
| [D] "The tree chose A — we're done." | Holds only at assigned probabilities. Sensitivity analysis is mandatory before concluding. |
| [D] "We enumerated all branches." | Trees are always simplifications. Ask explicitly: what branches are missing? |
| [D] "It's 50/50 — we just don't know." | 50/50 is a claim requiring justification. What base rate supports it? |
| [D] "My gut says B even though the tree says A." | Gut = implicit tree with different probabilities. Find which number your gut is using and put it in. |
| [D] "The tree gave a recommendation — it must be right." | GIGO: garbage probabilities produce garbage recommendations. Calibrate inputs first. |
| → Add [O] entries here after each real use — paste the actual failure pattern | What went wrong and why |
Red Flags
- Probabilities verbal only — no numbers written | Chance-node probabilities don't sum to 1.0
- No sensitivity analysis performed | Terminal payoffs in mixed units across branches
- Probabilities assigned post-hoc to justify a pre-decided conclusion | No missing-branch audit
Verification
- Root decision defined; all options enumerated
- All chance nodes MECE; probabilities sum to 1.0 at each node
- Every probability has documented basis
- All terminal payoffs in same unit and discount rate
- Rollback verified numerically at every node
- Sensitivity complete — switchover threshold identified
- EVPI calculated; data-gathering decision made
- Missing-branch audit performed; recommendation states conditions it holds and flips
Part of deciqAI Knowledge Skills — 237 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/s/decision-tree · Built by deciqAI · github.com/deciqAI · Contributions welcome.
Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/decision-tree.json