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Activate when: 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 sequent...
--- name: decision-tree description: "Activate when: 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'. Do NOT activate when: 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. More: deciqai.com/c/decision-tree" --- # 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. 1. **One-line:** a decision tree converts "I feel" into "what probability do you assign?" — making assumptions explicit so they can be argued about. 2. **Check fit.** Sequential stages? Uncertain outcomes? Quantifiable payoffs? If yes to all three, a tree applies. 3. **Elicit their real case.** What's the initial choice? What uncertain outcomes follow? What payoffs result? > **[WAIT — do not advance until user responds]** 4. **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]** 5. **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)](examples/magee-1964-chemical-plant-investment-hbr.md) · [Eisenhower's D-Day Weather Decision](examples/eisenhower-1944-d-day-weather-decision.md)* *→ 2026 lens: [A Chipmaker's Leading-Edge Fab Investment Under AI Uncertainty (2024–2026)](examples/chipmaker-leading-edge-fab-investment-2024-2026.md)* ## 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](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** — 227 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/c/decision-tree** · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.* *Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/decision-tree.json*
don't have the plugin yet? install it then click "run inline in claude" again.
separated intent from overview, formalized inputs (payoff unit, discount rate, risk appetite), broke procedure into six explicit steps with clear inputs/outputs, extracted decision logic into explicit if-else branches, standardized output contract with verification checklist, added concrete outcome signals tied to user behavior rather than skill execution.
a decision tree converts multi-stage choices into explicit, quantified models: decision nodes (squares) for choices you control, chance nodes (circles) for outcomes you don't, probabilities on every branch, payoffs at the leaves. you roll back right-to-left to compute expected value at the root. use it when the decision has sequential stages (decide, learn, decide again), outcomes are uncertain but probabilities can be estimated, and payoffs are quantifiable. the core value: turning "I feel we should expand" into "what probability do you assign to high demand?" , making every assumption visible and contestable. do not use for one-shot choices, unestimable probabilities, purely qualitative payoffs, or branch sets too large for analysis (use scenario planning instead).
no external integrations required. all computation is manual (spreadsheet-ready or pencil-and-paper).
Step 1: root decision. write down the initial choice , what are your options? what's the timeline? draw a square. each option is a branch leaving the square.
Step 2: chance nodes. for each option branch, identify uncertain events that could follow. draw a circle for each. label each circle with the event (e.g., "market demand", "competitor entry", "technical success"). branches leaving each circle must be mutually exclusive and collectively exhaustive (MECE) , no gaps, no overlaps.
Step 3: probabilities. assign a number (0.0 to 1.0) to every branch leaving every chance node. at each node, probabilities must sum to exactly 1.0. document the basis for every number: base rate, expert judgment, historical data, forecast. reject "50/50" without justification. if you have no basis, say so and note it as a calibration risk.
Step 4: payoffs. walk to every terminal leaf (end of every path). assign a payoff in your chosen unit (NPV, revenue, cost, etc.). payoffs must be in the same unit and, if multi-year, discounted at your specified rate. include opportunity costs and sunk cost exclusions.
Step 5: rollback. starting at the rightmost nodes, work left. at each chance node, compute expected value = sum of (probability × value) for all branches leaving that node. at each decision node (square), compare the expected values of all branches; mark the highest EV as optimal, mark others as rejected (//).
Step 6: sensitivity and stop-rule. find the probability threshold at which the optimal choice switches. compute EVPI = EV(perfect information) minus EV(best decision now). if EVPI is less than the cost of gathering more data, decide now. if EVPI exceeds the cost, gather data first. do not refine further once the leading option's EV advantage exceeds the value of further analysis.
deliver output in this format:
Decision Tree: <topic>
Options: <list all root branches> | Timeline: <decision horizon>
Tree Structure:
<node-by-node description, indented, showing hierarchy>
Probabilities:
Node | Branch | Probability | Basis
[row per branch, probabilities at each node must sum to 1.0]
Payoffs:
Path | Value | Unit
[row per terminal path]
Rollback:
Option A EV = [calculated value]
Option B EV = [calculated value]
[... all options]
Optimal = [option name] at assigned probabilities
Sensitivity:
Recommendation flips when [key probability] > [threshold]
EVPI = [calculated value] at current probabilities
Data-gathering decision: [decide now / gather data on [specific uncertainty] first]
Recommendation:
Choose [option]. This holds if [condition on probabilities/payoffs].
Flips if [condition on probabilities/payoffs].
Missing-branch audit:
[List branches you explicitly did not model and why, or confirm exhaustiveness]
payoffs must be in consistent units. rollback calculations must be verifiable by the user. sensitivity must include the switchover threshold and EVPI. every row must have a basis or "not documented" flag.
you know the skill worked when:
if you run the skill and the user leaves without any of the above, the skill did not work. re-run sensitivity, re-audit branches, or pivot to a different tool.
credits: original deciqAI decision-tree skill. enriched to meet Implexa standards, 2025.