Decision tree analysis for complex decision-making across all domains. Use when user needs to evaluate multiple options with uncertain outcomes, assess risk/reward scenarios, or structure choices syst
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Decision tree analysis: a visual tool for making decisions with probabilities and expected value.
✅ Good for:
❌ Not suitable for:
Decision tree = tree-like structure where:
Process:
EV = Σ (probability_i × value_i)
Example:
Decision: Go to party or stay home?
Decision ├─ Go to party │ ├─ Take jacket │ │ ├─ Cold (70%) → 9 utility (party) │ │ └─ Warm (30%) → 9 - 2 = 7 utility (carried unnecessarily) │ │ EV = 0.7 × 9 + 0.3 × 7 = 8.4 │ └─ Don't take jacket │ ├─ Cold (70%) → 9 - 10 = -1 utility (froze) │ └─ Warm (30%) → 9 utility (perfect) │ EV = 0.7 × (-1) + 0.3 × 9 = 2.0 └─ Stay home └─ EV = 3.0 (always)
Conclusion: Go and take jacket (EV = 8.4) > stay home (EV = 3.0) > go without jacket (EV = 2.0)
Decision: Launch new product?
Launch product ├─ Success (40%) → +$500K └─ Failure (60%) → -$200KEV = (0.4 × 500K) + (0.6 × -200K) = 200K - 120K = +$80K
Don't launch └─ EV = $0
Conclusion: Launch (EV = +$80K) is better than not launching ($0).
Decision: Enter position or wait?
Enter position ├─ Rise (60%) → +$100 └─ Fall (40%) → -$50EV = (0.6 × 100) + (0.4 × -50) = 60 - 20 = +$40
Wait └─ No position → $0
EV = $0
Conclusion: Entering position has positive EV (+$40), better than waiting ($0).
⚠️ Critical points:
But: The method is valuable for structuring thinking, even if numbers are approximate.
Ask:
Help estimate through:
Draw tree in markdown:
Decision ├─ Option A │ ├─ Outcome A1 (X%) → Value Y │ └─ Outcome A2 (Z%) → Value W └─ Option B └─ Outcome B1 (100%) → Value V
For each option:
EV_A = (X% × Y) + (Z% × W) EV_B = V
Option with highest EV = best choice (rationally).
But add context:
Position Sizing:
Entry Timing:
Product Launch:
Hiring Decision:
Career Change:
Real Estate:
Capacity Planning:
Vendor Selection:
Use
scripts/decision_tree.pypython3 scripts/decision_tree.py --interactive
Or via JSON:
python3 scripts/decision_tree.py --json tree.json
JSON format:
{ "decision": "Launch product?", "options": [ { "name": "Launch", "outcomes": [ {"name": "Success", "probability": 0.4, "value": 500000}, {"name": "Failure", "probability": 0.6, "value": -200000} ] }, { "name": "Don't launch", "outcomes": [ {"name": "Status quo", "probability": 1.0, "value": 0} ] } ] }
Output:
📊 Decision Tree AnalysisDecision: Launch product?
Option 1: Launch └─ EV = $80,000.00 ├─ Success (40.0%) → +$500,000.00 └─ Failure (60.0%) → -$200,000.00
Option 2: Don't launch └─ EV = $0.00 └─ Status quo (100.0%) → $0.00
✅ Recommendation: Launch (EV: $80,000.00)
Before giving recommendation, ensure:
✅ Simple — people understand trees intuitively ✅ Visual — clear structure ✅ Works with little data — can use expert estimates ✅ White box — transparent logic ✅ Worst/best case — extreme scenarios visible ✅ Multiple decision-makers — can account for different interests
❌ Unstable — small data changes → large tree changes ❌ Inaccurate — often more precise methods exist ❌ Subjective — probability estimates "from the head" ❌ Complex — becomes unwieldy with many outcomes ❌ Doesn't account for risk preference — assumes risk neutrality
The method is valuable for structuring thinking, but numbers are often taken from thin air.
What matters more is the process — forcing yourself to think through all branches and explicitly evaluate consequences.
Don't sell the decision as "scientifically proven" — it's just a framework for conscious choice.
No automatic installation available. Please visit the source repository for installation instructions.
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