---
id: wiki-2026-0508-issue-tree
title: Issue Tree
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Logic Tree, Hypothesis Tree, Decomposition Tree]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [problem-solving, consulting, decomposition, mece]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: markdown
  framework: mece
---

# Issue Tree

## 매 한 줄
> **"매 problem 매 MECE branches 의 decompose"**. Issue tree 매 root question 매 sub-question (mutually exclusive, collectively exhaustive) 의 hierarchical breakdown — 매 Minto Pyramid + McKinsey 80년 standard. 매 2026 LLM agent task planning, root-cause analysis 매 동일 pattern.

## 매 핵심

### 매 종류
- **Diagnostic (Why)**: 매 cause 의 decompose. 매 5-Why 의 generalized.
- **Solution (How)**: 매 action option 의 decompose.
- **Hypothesis tree**: 매 testable claim 의 분기 — 매 consulting deliverable.
- **Profitability tree**: Profit = Revenue − Cost — 매 standard MBA template.

### 매 작성 원칙
- **MECE**: 매 branches 매 overlap X, exhaustive O.
- **Same level abstraction**: 매 sibling 매 동일 granularity.
- **Verb-noun structure**: 매 action-oriented (solution tree).
- **Falsifiable leaves**: 매 leaf 매 data-checkable hypothesis.

### 매 응용
1. 매 management consulting (case interview).
2. 매 incident root cause (post-mortem).
3. 매 LLM agent — task decomposition (ReAct, Tree-of-Thoughts).
4. 매 product strategy (Jobs-to-be-Done).

## 💻 패턴

### 1. Profitability Tree (Markdown)
```markdown
- Profit decline?
  - Revenue down?
    - Volume down?
      - Market shrink?
      - Share loss?
    - Price down?
      - Discount increase?
      - Mix shift?
  - Cost up?
    - COGS up?
    - SG&A up?
```

### 2. 5-Why (Diagnostic)
```python
def five_why(problem):
    chain = [problem]
    for _ in range(5):
        cause = ask("Why?", context=chain[-1])
        chain.append(cause)
    return chain  # 매 root cause 매 마지막
```

### 3. Hypothesis-Driven (McKinsey-style)
```yaml
root: "Should we enter market X?"
children:
  - "Is the market attractive?"
    children:
      - "TAM > $1B?" [data: industry report]
      - "Growth > 10%?" [data: historical CAGR]
      - "Margins > 20%?" [data: comparable companies]
  - "Can we win?"
    children:
      - "Right-to-play assets?"
      - "Competitive advantage sustainable?"
  - "Is it worth it?"
    children:
      - "NPV > $X?"
      - "Strategic fit?"
```

### 4. Tree-of-Thoughts (LLM)
```python
def tot(problem, depth=3, branches=3):
    if depth == 0:
        return evaluate(problem)
    sub_problems = llm_decompose(problem, k=branches)
    scores = [tot(sp, depth-1, branches) for sp in sub_problems]
    return max(zip(sub_problems, scores), key=lambda x: x[1])
```

### 5. Fishbone (Ishikawa) — alternative form
```
                    ┌── People ── Training gap
                    │
Defect ─────────────┼── Process ── No QA gate
                    │
                    └── Tooling ── Outdated CI
```

### 6. Markdown Renderer (Mermaid)
```mermaid
graph TD
  Root["Why is churn up?"] --> A["Product issue?"]
  Root --> B["Pricing issue?"]
  Root --> C["Support issue?"]
  A --> A1["Bug rate up"]
  A --> A2["Feature gap"]
  B --> B1["Competitor cheaper"]
```

## 매 결정 기준
| 상황 | Tree type |
|---|---|
| Find root cause | Diagnostic / 5-Why |
| Choose action | Solution tree |
| Strategy decision | Hypothesis tree |
| LLM task decomp | Tree-of-Thoughts |
| Manufacturing defect | Ishikawa |

**기본값**: 매 hypothesis tree (testable leaves) — 매 consulting/strategy 매 standard.

## 🔗 Graph
- 부모: [[Problem Solving]] · [[Strategic Thinking]]
- 변형: [[Logic Trees]] · [[Mental Models]]
- 응용: [[Root-Cause-Analysis-RCA]] · [[Mutually Exclusive and Collectively Exhaustive (MECE)]] · [[Profitability Framework]]
- Adjacent: [[Decision Theory]] · [[Tree-of-Thoughts]]

## 🤖 LLM 활용
**언제**: 매 task decomposition, 매 root-cause investigation, 매 case-interview prep, 매 ToT/agent planning.
**언제 X**: 매 single-step factual lookup. 매 over-decompose 의 paralysis.

## ❌ 안티패턴
- **Non-MECE branches**: 매 overlap 또는 gap.
- **Mixed abstraction**: 매 sibling 매 inconsistent depth.
- **No data plan**: 매 leaf 매 untestable.
- **Pre-determined answer**: 매 tree 매 confirmation bias 의 disguise.

## 🧪 검증 / 중복
- Verified (Minto, *The Pyramid Principle*; McKinsey *Problem Solving*).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — profitability/hypothesis/ToT patterns |
