docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거
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Antigravity Agent
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---
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id: wiki-2026-0508-system-theory
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title: System Theory
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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, General Systems Theory, GST, 시스템 이론, Systems Thinking]
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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-theory, cybernetics, complexity, emergence, feedback]
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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: python
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framework: networkx
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---
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# System Theory
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## 매 한 줄
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> **"매 system theory 는 components 의 isolation 의 X — 매 interaction·feedback·emergence 의 study"**. 매 von Bertalanffy "General System Theory" (1968) 의 origin, Wiener cybernetics 의 partner. 매 2026 의 modern descendants: complex systems, network science, dynamical systems, software architecture (microservice, observability).
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## 매 핵심
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### 매 foundational figures
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- **Ludwig von Bertalanffy (1968)**: General System Theory — 매 biology·sociology·engineering 의 unify.
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- **Norbert Wiener (1948)**: Cybernetics — feedback 의 first-class.
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- **Ashby**: Law of Requisite Variety — 매 controller variety ≥ system variety.
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- **Forrester**: System Dynamics — stock·flow·delay model.
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- **Senge (1990)**: "Fifth Discipline" — organizational systems thinking.
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- **Meadows (2008)**: "Thinking in Systems" — leverage points.
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### 매 core concepts
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- **System = elements + interconnections + purpose** (Meadows).
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- **Open vs closed**: open = energy/info exchange with env; biology, business 매 open.
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- **Feedback loop**: balancing (B, negative) → stabilize. reinforcing (R, positive) → exponential.
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- **Stock & flow**: state (stock) 의 flow rate 의 integral.
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- **Delay**: 매 oscillation·overshoot 의 source.
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- **Emergence**: macro property 의 micro 의 absent.
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- **Equifinality**: 매 다른 path 의 same end state.
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### 매 leverage points (Meadows, low → high)
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1. Numbers (parameters).
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2. Buffers.
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3. Stock-flow structure.
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4. Delays.
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5. Balancing loops.
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6. Reinforcing loops.
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7. Information flows.
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8. Rules.
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9. Self-organization.
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10. Goals.
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11. Paradigms.
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12. Power to transcend paradigms.
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### 매 modern descendants
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- **Complex Systems**: Santa Fe Institute — Holland, Kauffman.
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- **Network Science**: Barabási — scale-free, small-world.
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- **Dynamical Systems**: Strogatz — nonlinear, chaos, attractor.
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- **Software**: SRE (feedback via SLO), DDD (bounded context = subsystem), observability.
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### 매 응용
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1. **Software architecture**: microservice = subsystem + bounded context.
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2. **SRE**: error budget = balancing loop on reliability.
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3. **Climate / business**: System Dynamics simulation (Vensim, Stella).
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4. **AI safety**: feedback loop analysis for reward hacking, recursive improvement.
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## 💻 패턴
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### 1. Stock & flow simulation (System Dynamics)
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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 simulate(birth_rate=0.05, death_rate=0.02, init_pop=1000, T=100, dt=0.1):
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pop = np.zeros(int(T/dt))
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pop[0] = init_pop
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for t in range(1, len(pop)):
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births = birth_rate * pop[t-1] * dt # reinforcing
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deaths = death_rate * pop[t-1] * dt # balancing
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pop[t] = pop[t-1] + births - deaths
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return pop
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pop = simulate()
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plt.plot(pop); plt.title("Population stock")
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```
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### 2. Feedback loop classification
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```python
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def classify_loop(edges):
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"""edges: list of (src, dst, sign in {+1, -1})"""
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product = 1
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for _, _, sign in edges:
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product *= sign
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return "reinforcing (R)" if product > 0 else "balancing (B)"
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```
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### 3. Lotka-Volterra (predator-prey, dynamical system)
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```python
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from scipy.integrate import odeint
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def lv(state, t, alpha, beta, delta, gamma):
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x, y = state # prey, predator
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dxdt = alpha*x - beta*x*y
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dydt = delta*x*y - gamma*y
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return [dxdt, dydt]
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t = np.linspace(0, 50, 1000)
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sol = odeint(lv, [40, 9], t, args=(0.1, 0.02, 0.01, 0.1))
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```
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### 4. Network analysis (small-world, scale-free)
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```python
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import networkx as nx
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# Watts-Strogatz small-world
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G_sw = nx.watts_strogatz_graph(n=1000, k=10, p=0.1)
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print("avg path:", nx.average_shortest_path_length(G_sw))
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print("clustering:", nx.average_clustering(G_sw))
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# Barabási-Albert scale-free
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G_ba = nx.barabasi_albert_graph(n=1000, m=3)
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degrees = [d for _, d in G_ba.degree()]
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# power-law degree distribution
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```
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### 5. Causal Loop Diagram (CLD) as graph
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```python
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import networkx as nx
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cld = nx.DiGraph()
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cld.add_edge("ad spend", "leads", sign=+1)
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cld.add_edge("leads", "revenue", sign=+1)
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cld.add_edge("revenue", "ad spend", sign=+1) # R loop
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cld.add_edge("ad spend", "cash", sign=-1)
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cld.add_edge("cash", "ad spend", sign=+1) # B loop on cash
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for cycle in nx.simple_cycles(cld):
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edges = [(cycle[i], cycle[(i+1) % len(cycle)]) for i in range(len(cycle))]
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signs = [cld[u][v]["sign"] for u, v in edges]
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loop_type = "R" if np.prod(signs) > 0 else "B"
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print(cycle, loop_type)
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```
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### 6. SLO error budget (SRE balancing loop)
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```python
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class ErrorBudget:
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def __init__(self, slo=0.999, window_days=30):
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self.budget = (1 - slo) * window_days * 24 * 60 # minutes
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self.consumed = 0
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def consume(self, downtime_min):
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self.consumed += downtime_min
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return self.consumed >= self.budget # halt deploys when burned
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def remaining(self):
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return max(0, self.budget - self.consumed)
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```
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### 7. Leverage point identification
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```python
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LEVERAGE = {
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"params": 1, "buffer": 2, "structure": 3, "delays": 4,
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"balancing": 5, "reinforce": 6, "info": 7, "rules": 8,
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"self_org": 9, "goals": 10, "paradigm": 11, "transcend": 12,
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}
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def rank_intervention(name, kind):
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return LEVERAGE.get(kind, 0)
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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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| Continuous quantities, feedback | System Dynamics (Vensim/PySD) |
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| Discrete agents, interaction | Agent-Based Model (Mesa) |
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| Network of relationships | Network Science (NetworkX) |
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| Software subsystem boundary | DDD bounded context |
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| Reliability | SRE error budget loops |
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**기본값**: 매 problem framing 의 first — stock/flow/loop 의 sketch, 그 후 quantitative tool 의 select.
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## 🔗 Graph
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- 부모: [[Cybernetics Foundations|Cybernetics]] · [[Complexity-Science]]
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- 응용: [[SRE]]
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- Adjacent: [[Synergy]] · [[Emergence]] · [[Feedback-Loop]] · [[Domain-Driven-Design]]
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## 🤖 LLM 활용
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**언제**: CLD 의 sketch from natural language description, leverage point 의 explain, scenario walk-through.
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**언제 X**: quantitative simulation 의 itself (PySD/Mesa 의 사용 — LLM 의 number drift).
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## ❌ 안티패턴
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- **Linear thinking on systems**: 매 cause→effect chain 의 only — feedback 의 ignore.
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- **Local optimization**: 매 sub-optimum globally — Goodhart's law.
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- **Delays 무시**: 매 oscillation·overshoot 의 surprise.
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- **Numbers (params) 의 leverage 의 prioritize**: 매 lowest leverage — paradigm·goals 의 더 powerful.
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- **Closed-system assumption on open**: 매 boundary 의 false → ignored externalities.
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## 🧪 검증 / 중복
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- Verified (Bertalanffy "General System Theory" 1968, Meadows "Thinking in Systems" 2008, Wiener "Cybernetics" 1948, Forrester "Industrial Dynamics" 1961).
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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 — system theory (Bertalanffy + cybernetics + modern complexity + software apps) |
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