2nd/10_Wiki/Topics/Computer_Science_and_Theory/Logic Trees.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-logic-trees	Logic Trees	10_Wiki/Topics	verified	self	Logic-Tree Issue-Tree Decision-Tree-Reasoning	none	A	0.9	applied	reasoning problem-solving mece consulting structured-thinking		2026-05-10	pending	language framework markdown unspecified

Logic Trees

매 한 줄

"매 문제를 MECE 가지로 쪼개라". McKinsey식 hypothesis-driven problem solving의 매 핵심 도구로, 매 root question을 mutually exclusive·collectively exhaustive subquestions으로 분할한다. 2026 LLM agent의 chain-of-thought planner와 매 동형(同形).

매 핵심

매 종류

• Why-tree (diagnostic): 매 root cause 분석 — "왜?"를 5번 반복.
• How-tree (solution): 매 목표 달성 방법 분해.
• What-tree (descriptive): 매 system component 분류.

매 구성 원칙

• MECE: 매 가지가 서로 겹치지 않고 (ME) 매 합쳐서 전체 (CE).
• Same-level abstraction: 매 형제 노드는 same granularity.
• Hypothesis-driven: 매 leaf에 매 testable hypothesis.

매 응용

1. Consulting case interview (McKinsey, BCG, Bain).
2. Root cause analysis (5 Whys, Fishbone).
3. Product strategy (revenue tree: price × volume).
4. LLM agent task decomposition.

💻 패턴

Markdown logic tree

- Root: 왜 매출이 감소했나?
  - 가격 하락? (Hypothesis 1)
    - 평균 단가 추이 확인
    - 할인율 변화 확인
  - 거래량 감소? (Hypothesis 2)
    - 신규 고객 수
    - 재구매율
  - 믹스 변화? (Hypothesis 3)
    - 카테고리별 비중

Revenue tree (Python dataclass)

from dataclasses import dataclass
@dataclass
class RevenueTree:
    revenue: float
    price: float
    volume: int
    @classmethod
    def decompose(cls, rev, p, v):
        assert abs(rev - p * v) < 1e-6
        return cls(rev, p, v)

LLM agent decomposition prompt

prompt = """
Decompose this problem into a MECE logic tree (depth=3).
Each leaf must be a testable hypothesis.
Problem: {problem}
Output: nested JSON with {hypothesis, subnodes, test_method}.
"""

Tree validator (MECE check)

def is_mece(children, total):
    coverage = sum(c.share for c in children)
    overlap = any(set(a.scope) & set(b.scope)
                  for a, b in itertools.combinations(children, 2))
    return abs(coverage - 1.0) < 0.01 and not overlap

Issue tree → Gantt

def tree_to_tasks(node, owner=None):
    tasks = []
    if not node.children:
        tasks.append({"task": node.q, "owner": owner})
    for c in node.children:
        tasks.extend(tree_to_tasks(c, owner=c.owner))
    return tasks

매 결정 기준

상황	Tree type
문제 진단	Why-tree
해결책 설계	How-tree
시스템 이해	What-tree
LLM agent plan	How-tree (with tools)

기본값: Hypothesis-driven Why-tree (depth 3-4).

🔗 Graph

• 부모: Mental_Models · Mutually Exclusive and Collectively Exhaustive (MECE)
• 변형: Issue Tree · Decision Theory
• 응용: Root Cause Analysis · 5-Whys
• Adjacent: Pyramid Principle

🤖 LLM 활용

언제: Open-ended problem decomposition, agent task planning, structured analysis. 언제 X: Tightly coupled problems with no clean partition (use system dynamics).

❌ 안티패턴

• Non-MECE branches: 매 overlap 또는 gap.
• Symptom tree: 매 root cause 도달 전에 멈춤.
• Boil-the-ocean: 매 prioritization 없이 매 leaf 다 조사.

🧪 검증 / 중복

• Verified (Minto "The Pyramid Principle"; Rasiel "The McKinsey Way").
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — Logic tree taxonomy + agent integration