refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-systems-thinking
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title: Systems Thinking
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Systems Theory, Holistic Thinking, Feedback Loops]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [systems, thinking, modeling, complexity]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: methodology
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framework: causal-loop
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---
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# Systems Thinking
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## 매 한 줄
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> **"매 part 의 sum 의 X — 매 interaction 의 emergent behavior"**. 매 element 의 isolated analysis 의 대신 매 stocks, flows, feedback loops, delays 의 holistic 의 see. Donella Meadows 의 "Thinking in Systems" 의 canonical — 2026 의 software, climate, policy 의 적용.
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## 매 핵심
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### 매 핵심 vocabulary
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- **Stock**: 매 accumulation (inventory, $, customers, tech debt).
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- **Flow**: 매 rate of change (sales/day, hires/month).
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- **Feedback loop**: 매 output 의 input 의 영향.
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- **Reinforcing (R)**: amplifies — 매 viral growth.
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- **Balancing (B)**: stabilizes — 매 thermostat.
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- **Delay**: 매 cause → effect 의 lag — oscillation 의 cause.
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### 매 leverage points (Meadows, ranked)
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1. Paradigm (mindset).
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2. Goals of system.
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3. Self-organization rules.
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4. Information flows.
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5. Rules / incentives.
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6. Negative feedback strength.
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7. Positive feedback strength.
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8. Material flows / stocks.
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9. Numbers / parameters (lowest leverage).
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### 매 archetypes
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- **Limits to Growth**: R + B → S-curve.
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- **Shifting the Burden**: short-term fix 의 weakens long-term solution.
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- **Tragedy of the Commons**: shared resource 의 overuse.
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- **Fixes That Fail**: 매 quick fix 의 root cause 의 worsen.
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- **Success to the Successful**: rich-get-richer.
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- **Escalation**: arms race.
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### 매 응용
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1. Software incident analysis (alerts → fatigue → ignore).
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2. Tech debt dynamics (speed ↔ debt ↔ slowdown).
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3. Org design (incentives → behavior → outcomes).
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4. Climate / policy modeling.
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## 💻 패턴
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### Causal Loop Diagram (CLD) — text format
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```
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Tech debt:
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velocity (-) → tech_debt // less time to fix → debt grows
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tech_debt (-) → velocity // more debt → slower work
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// R loop: vicious cycle
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tech_debt (+) → cleanup_priority
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cleanup_priority (-) → tech_debt
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// B loop: self-correcting (if priority actually given)
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```
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### Stock-flow — Python (simple SIR)
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```python
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import numpy as np
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import matplotlib.pyplot as plt
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def sir(beta=0.3, gamma=0.1, S0=999, I0=1, R0=0, days=160, dt=1):
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S, I, R = [S0], [I0], [R0]
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N = S0 + I0 + R0
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for _ in range(int(days/dt)):
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dS = -beta * S[-1] * I[-1] / N
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dI = beta * S[-1] * I[-1] / N - gamma * I[-1]
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dR = gamma * I[-1]
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S.append(S[-1] + dS*dt)
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I.append(I[-1] + dI*dt)
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R.append(R[-1] + dR*dt)
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return S, I, R
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S, I, R = sir()
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plt.plot(I, label='Infected')
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plt.show()
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```
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### Limits to Growth — code
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```python
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def growth_with_limit(K=1000, r=0.1, P0=10, T=200):
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P = [P0]
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for _ in range(T):
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# R loop: r*P (reinforcing)
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# B loop: (1 - P/K) (balancing as P → K)
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dP = r * P[-1] * (1 - P[-1] / K)
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P.append(P[-1] + dP)
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return P
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# Logistic curve: explosive then plateau
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```
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### Behavior over time graph (BoT)
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```
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^ ___
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| __/
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| __/ ← logistic (limits to growth)
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| ___/
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| ___/
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| ___/
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| __/
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| __/
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| /
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+----------------------------> time
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```
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### Archetype detector — checklist
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```yaml
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shifting_the_burden:
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symptoms:
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- "Quick fix repeatedly applied"
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- "Underlying problem persists or worsens"
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- "Capability for fundamental fix atrophies"
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examples:
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- Painkillers vs. cause
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- Hotfixes vs. refactoring
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- Hiring more on-call vs. reducing alerts
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```
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### Causal vs. correlational
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```python
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# Bad: regression on snapshot
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# Good: simulate stocks/flows over time, validate
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# with both reference modes (BoT) and structure
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Persistent recurring issue | Identify archetype |
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| Counter-intuitive outcome | Look for delays / loops |
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| Quick decision | Mental CLD sketch |
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| Forecast / policy test | Stock-flow simulation |
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| Single-cause obvious | 매 systems thinking 의 overkill |
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**기본값**: 매 CLD sketch 의 first — 매 archetype 의 명확 시 의 simulation.
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## 🔗 Graph
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- 부모: [[Cybernetics Foundations|Cybernetics]]
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- 응용: [[Tech Debt]]
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- Adjacent: [[Feedback Loops]] · [[Mental_Models|Mental Models]] · [[Causal Inference]]
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## 🤖 LLM 활용
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**언제**: archetype identification, CLD draft from narrative, 매 hidden loops 의 surface.
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**언제 X**: quantitative simulation (use Vensim, Stella, SimPy) — LLM 의 numeric simulation 의 unreliable.
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## ❌ 안티패턴
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- **Linear thinking on systemic problem**: 매 single cause 의 search — 매 loop 의 miss.
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- **Ignore delays**: 매 oscillation 의 surprise — 매 delay 의 model.
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- **Optimize parts in isolation**: 매 local optim 의 global degrade.
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- **Paralysis by complexity**: 매 over-modeling — 매 핵심 archetype 의 enough.
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## 🧪 검증 / 중복
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- Verified (Donella Meadows "Thinking in Systems"; Senge "5th Discipline"; Sterman "Business Dynamics").
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — stocks/flows, archetypes, CLD, leverage points |
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