Decision Trees — Structured Decision-Making | Skills Pool
스킬 파일
Decision Trees — Structured Decision-Making
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 systematically. Applicable to business, investment, personal decisions, operations, career choices, product strategy, and any situation requiring structured evaluation. Triggers include decision tree, should I, what if, evaluate options, compare alternatives, risk analysis.
sundial-org576 스타2026. 2. 1.
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스킬 내용
Decision tree analysis: a visual tool for making decisions with probabilities and expected value.
When to Use
✅ Good for:
Business decisions (investments, hiring, product launches)
Personal choices (career, relocation, purchases)
Trading & investing (position sizing, entry/exit)
Operational decisions (expansion, outsourcing)
Any situation with measurable consequences
❌ Not suitable for:
Decisions with true uncertainty (black swans)
Fast tactical choices
Purely emotional/ethical questions
Method
Decision tree = tree-like structure where:
Decision nodes (squares) — your actions
Chance nodes (circles) — random events
End nodes (triangles) — final outcomes
Process:
Define options — all possible actions
관련 스킬
Define outcomes — what can happen after each action
Estimate probabilities — how likely is each outcome (0-100%)
Estimate values — utility/reward for each outcome (money, points, utility units)
Calculate EV — expected value = Σ (probability × value)
📊 Decision Tree Analysis
Decision: 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)
Final Checklist
Before giving recommendation, ensure:
✅ All options covered
✅ Probabilities sum to 100% for each branch
✅ Values are realistic (not fantasies)
✅ Worst case scenario is clear to user
✅ Risk/reward ratio is explicit
✅ Method limitations mentioned
✅ Qualitative context added (not just EV)
Method Advantages
✅ 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
Method Disadvantages
❌ 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
Important
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.
Further Reading
Decision trees in operations research
Influence diagrams (more compact for complex decisions)