2nd/10_Wiki/Topics/Computer_Science_and_Theory/Issue Tree.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

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

- 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)

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)

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)

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)

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