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2026-05-20 23:52:15 +09:00

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title

Mutually Exclusive and Collectively Exhaustive (MECE)

매 한 줄

"매 항목 겹치지 않고 매 합쳐 전체". Barbara Minto가 McKinsey에서 정형화한 매 categorization 원칙으로, 매 합리적 의사소통과 매 logical decomposition의 매 backbone이다. 2026 LLM agent의 task partitioning에서도 매 동일 원칙이 적용된다.

매 핵심

매 두 조건

Mutually Exclusive (ME): 매 두 카테고리의 교집합 = ∅.
Collectively Exhaustive (CE): 매 카테고리들의 합집합 = 전체.

매 typical partition 패턴

Binary split: A vs Not-A.
Process steps: Before / During / After.
Stakeholders: Internal / External.
Time: Past / Present / Future.
Quantitative axes: Price × Volume = Revenue.

매 응용

Issue tree / logic tree decomposition.
Market sizing (top-down vs bottom-up).
SQL GROUP BY 카테고리 설계.
LLM agent subtask split (no overlap, no gap).

💻 패턴

MECE check (Python)

def is_mece(categories, universe):
    union = set().union(*categories)
    if union != universe:
        return False, "not exhaustive", universe - union
    for i, a in enumerate(categories):
        for b in categories[i+1:]:
            if a & b:
                return False, "overlap", a & b
    return True, "MECE", None

Decision-tree style decomposition

- 매출 감소 원인
  - 가격 (P)
  - 수량 (Q) [P와 ME — 가격 효과 통제 후]
  - 믹스 (M) [구성 변화]
  → P + Q + M ≈ ΔRevenue (CE check)

Pandas group MECE validation

def validate_mece_groups(df, group_col, total_col):
    grouped = df.groupby(group_col)[total_col].sum()
    return abs(grouped.sum() - df[total_col].sum()) < 1e-6

Set cover (greedy CE construction)

def greedy_cover(universe, sets):
    chosen, remaining = [], set(universe)
    while remaining:
        best = max(sets, key=lambda s: len(s & remaining))
        chosen.append(best)
        remaining -= best
    return chosen

LLM prompt for MECE plan

prompt = """
Decompose the task into MECE subtasks:
- Subtasks must NOT overlap (ME).
- Subtasks must cover the full task (CE).
- Output JSON list. Verify by sketching the union set.
Task: {task}
"""

MECE + Pareto (80/20 prioritization)

def mece_pareto(buckets, values, top=0.8):
    sorted_buckets = sorted(zip(buckets, values), key=lambda x: -x[1])
    cum, picked = 0, []
    for b, v in sorted_buckets:
        picked.append(b); cum += v
        if cum >= top * sum(values): break
    return picked

매 결정 기준

상황	Decomposition axis
Revenue analysis	P × Q × M
Cost analysis	Fixed vs Variable
Customer	New vs Existing
Process	Stage gates
Bug triage	Severity × Component

기본값: Quantitative MECE (numerical sum-check 가능).

🔗 Graph

🤖 LLM 활용

언제: Task decomposition, structured analysis, taxonomy design, agent planning. 언제 X: Highly entangled / coupled systems where clean partition is impossible.

❌ 안티패턴

Pseudo-MECE: 매 표면 MECE이나 매 실제 overlap.
Forced exhaustiveness: "Other" 버킷으로 매 모든 잔여 처리.
Wrong axis: 매 분석 목적과 무관한 축.

🧪 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 — MECE conditions + validators

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