2nd/10_Wiki/Topic_Programming/Architecture/Mental_Models.md

---
id: wiki-2026-0508-mental-models
title: Mental Models
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [멘탈 모델, Cognitive Models, Thinking Frameworks]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [thinking, decision-making, productivity, learning]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: na
  framework: cognitive
---

# Mental Models

## 매 한 줄
> **"매 model 은 reality 의 simplified map — 매 right model 은 right decision."**. Mental model 은 매 사람이 세계를 이해하기 위해 head 안에 가진 representation. Charlie Munger 가 매 popularize — 매 multidisciplinary toolkit 으로 매 50-100 개의 model 을 갖추면 매 cross-domain reasoning 가능. 매 engineering, 매 product, 매 AI prompt 설계에 매 직접 적용.

## 매 핵심

### 매 종류 (engineering 관련)
- **First Principles**: 매 가정 분해, 매 fundamental physics/math 부터 reason.
- **Inversion**: 매 "어떻게 fail 할까" 부터 시작.
- **Second-order thinking**: 매 직접 결과 + 매 그 다음 결과까지.
- **Occam's Razor**: 매 simplest explanation 우선.
- **Hanlon's Razor**: 매 stupidity 가 malice 보다 매 흔하다.
- **Pareto (80/20)**: 매 20% 의 cause 가 매 80% 의 effect.

### 매 system thinking
- **Feedback loop**: reinforcing (snowball) vs balancing (thermostat).
- **Stock & flow**: state vs rate of change.
- **Leverage point**: 매 small change → 매 large outcome (Donella Meadows).

### 매 decision-making
- **Expected value**: 매 probability × payoff.
- **Regret minimization** (Bezos): "매 80세에 후회 안 할 결정?"
- **Reversible vs one-way door**: 매 undo 가능 → 빠르게 결정.
- **OODA loop**: Observe-Orient-Decide-Act (Boyd).

### 매 learning
- **Feynman technique**: 매 12살에게 설명할 수 있을 때까지.
- **Spaced repetition**: 매 forgetting curve 와 싸움 (Anki, SuperMemo).
- **Deliberate practice**: 매 edge of competence + immediate feedback.

## 💻 패턴

### First Principles 적용 (engineering)
```
문제: "DB query 가 느림"

❌ Analogical: "다른 팀은 cache 추가했음 → 우리도"

✅ First Principles:
  1. Query latency = network + parse + plan + execute + return
  2. 측정 → execute 가 95%
  3. EXPLAIN → seq scan on 10M rows
  4. Index → 20ms (was 2000ms)

→ Cache 는 매 next step (further reduction), 매 root cause 해결 후.
```

### Inversion (debugging)
```
"매 system 을 빠르게 만들 방법?"
→ "매 system 을 느리게 만드는 모든 방법?"
   - N+1 query
   - Sync 호출 in tight loop
   - Memory leak → GC pause
   - Lock contention
   - Network round-trip
→ 매 list 를 거꾸로 읽으면 optimization checklist.
```

### Second-order (product)
```
1차: "Feature X 추가 → user 늘어남"
2차: "user 늘어남 → support load 늘어남, infra cost 늘어남,
       기존 user 의 UX complexity 늘어남"
3차: "complexity → churn → 결국 user 감소 가능"

→ 매 1차만 보면 매 false positive.
```

### Pareto 적용 (LLM eval)
```python
# 80% of bugs from 20% of prompts
from collections import Counter
errors = load_eval_failures()
patterns = Counter([categorize(e) for e in errors])
top_20pct = patterns.most_common(int(len(patterns) * 0.2))
# → fix top 20% categories first → 80% of failures resolved
```

### Reversible decision matrix
```
| Decision           | Reversible? | Stakes | Approach           |
|--------------------|-------------|--------|--------------------|
| Library choice     | Yes (refactor) | Low | Pick + iterate     |
| Database schema    | Hard (migration) | High | Design carefully   |
| Hire               | No (mostly) | High | Slow, multiple sigs|
| Production rename  | Yes (alias) | Med  | Pick + monitor     |
```

### Feynman technique (learning a concept)
```
1. Pick concept (e.g., "Mark-Sweep GC")
2. Write down what you know — in plain language
3. Identify gaps where you used jargon
4. Go back to source (paper, doc), fill gap
5. Simplify until a 12-year-old understands
→ 매 gap exposure 가 매 핵심.
```

## 매 결정 기준
| 상황 | Model |
|---|---|
| 새 architecture 설계 | First Principles |
| Postmortem | Inversion ("어떻게 fail?") |
| Product decision | Second-order, Reversibility |
| Roadmap prioritization | Pareto, Expected Value |
| Learning new domain | Feynman |
| 여러 conflicting view | Steelman 후 weighted |

**기본값**: 매 small but diverse toolkit (10-15 models) 을 매 active recall — 매 50개 다 외우기보다 매 right one 을 right time 에 reach.

## 🔗 Graph
- 부모: [[Decision Making]]
- 변형: [[First Principles]] · [[Inversion]] · [[Systems_Thinking|Systems Thinking]]
- Adjacent: [[Cognitive Biases]] · [[Heuristics]]

## 🤖 LLM 활용
**언제**: complex problem 분해, multi-stakeholder decision, 새 domain learning, prompt 설계 (LLM 에게 model 명시 → 매 reasoning quality 상승).
**언제 X**: trivial / well-trodden problem — 매 over-thinking 의 risk.

## ❌ 안티패턴
- **One-model thinking** (Munger's "man with a hammer"): 매 모든 문제를 매 favorite model 로 — 매 distortion.
- **Analogical 만**: 매 "X 회사가 했으니 우리도" — 매 first principles 무시.
- **Model 수집만**: 매 50개 외우지만 매 active 사용 X — 매 deliberate practice 필요.
- **Confirmation bias** 와 결합: 매 favored model 로 매 cherry-pick.

## 🧪 검증 / 중복
- Verified (Munger "Poor Charlie's Almanack", Kahneman "Thinking Fast & Slow", Meadows "Thinking in Systems", Bezos shareholder letters).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — engineering 중심 mental model toolkit |