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-complexity-theory
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title: Complexity 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: [Complex Systems, Complexity Science]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.85
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verification_status: applied
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tags: [complexity, systems, emergence, cynefin]
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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: agnostic
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framework: agnostic
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---
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# Complexity Theory
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## 매 한 줄
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> **"매 system 의 behavior 가 매 part 의 sum 보다 크다 — 매 emergence, nonlinearity, feedback."**. Complexity theory는 Santa Fe Institute (1984~) 가 정립한 cross-disciplinary field. Software 에서는 Cynefin framework (Snowden), Brooks 의 essential vs accidental complexity, Promise Theory, distributed systems 의 emergent behavior 로 산다. 2026년 ML systems 의 emergent capabilities 도 매 핵심 case.
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## 매 핵심
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### 매 complex vs complicated
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| Complicated | Complex |
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|---|---|
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| Many parts, knowable | Many parts, emergent |
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| Aircraft, watch | Ecosystem, market, brain, microservices fleet |
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| Decompose & analyze | Probe → sense → respond |
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| Predictable | Unpredictable in detail |
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### 매 Cynefin (Snowden)
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- **Clear (Simple)**: cause→effect 자명. Best practice.
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- **Complicated**: expert analysis 필요. Good practice.
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- **Complex**: emergent, retrospective coherence. Probe → sense → respond.
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- **Chaotic**: no cause→effect. Act → sense → respond.
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- **Confusion** (Disorder): which domain unclear.
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### 매 essential vs accidental complexity (Brooks)
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- **Essential**: 매 problem itself 의 complexity (irreducible).
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- **Accidental**: tools, languages, infra 가 만든 complexity (reducible).
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- 매 silver bullet 없음 → essential complexity 의 tackling.
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### 매 emergent properties
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- Self-organization (ant colonies, market prices).
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- Phase transitions (water → ice, network connectivity).
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- Power laws (Zipf, scale-free networks).
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- Adaptive feedback (immune systems, ML training dynamics).
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### 매 응용 in software
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1. Microservice fleet behavior (cascading failures, retry storms).
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2. ML emergent capabilities (in-context learning at scale).
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3. Distributed consensus (CAP, FLP impossibility).
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4. Tech debt accumulation (compound complexity).
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5. Team scaling (Brooks' law as complexity manifestation).
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## 💻 패턴
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### Cynefin-driven decision (in code review)
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```python
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def categorize_problem(problem):
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if known_solution(problem):
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return "Clear: apply best practice"
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if expertise_resolves(problem):
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return "Complicated: expert analysis"
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if requires_experimentation(problem):
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return "Complex: probe-sense-respond"
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if no_cause_effect(problem):
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return "Chaotic: act-sense-respond"
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return "Disorder: clarify first"
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```
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### Probe → sense → respond (chaos engineering)
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```typescript
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// Netflix Chaos Monkey style — controlled probe of complex system
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import { ChaosClient } from '@netflix/chaos';
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const chaos = new ChaosClient();
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await chaos.experiment({
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name: 'kill-random-pod-payment-svc',
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hypothesis: 'system handles single pod loss within 30s',
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blast_radius: 'one pod',
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rollback_on: 'p99 > 500ms',
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observe: ['error_rate', 'latency_p99', 'saturation'],
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});
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```
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### Reduce accidental complexity — replace shell with compiled tool
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```python
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# Accidental: bash script with 5 sed/awk/jq pipes
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# Essential: extract user emails from JSON
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# After: simple, type-checked
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import json
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from pathlib import Path
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emails = [
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user["email"]
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for user in json.loads(Path("users.json").read_text())
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if user.get("active")
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]
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```
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### Feedback loop modeling (system dynamics)
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```python
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# Tech debt feedback loop — simple ODE
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import numpy as np
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from scipy.integrate import odeint
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def tech_debt(state, t, capacity, debt_growth, paydown_rate):
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debt, velocity = state
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d_debt = debt_growth - paydown_rate * velocity
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d_velocity = capacity * (1 - debt / 100) - velocity * 0.1
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return [d_debt, d_velocity]
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# Emergent: nonlinear collapse when debt > capacity
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sol = odeint(tech_debt, [10, 5], np.linspace(0, 100, 200),
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args=(10, 2, 0.5))
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```
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### Power-law detection (scale-free service dependency)
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```python
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import numpy as np
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import powerlaw
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# In-degree of microservice call graph
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in_degrees = compute_in_degrees(service_graph)
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fit = powerlaw.Fit(in_degrees)
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print(f"alpha={fit.power_law.alpha:.2f}") # ~2-3 → scale-free
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# Implication: targeted attack on hubs is catastrophic
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```
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### Promise Theory (Burgess) — autonomous agents
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```yaml
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# Each service makes promises, others assess
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service: payment-svc
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promises:
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- id: p99_latency_under_300ms
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conditions: [load < 1000rps]
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valid_until: 2026-12-31
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- id: idempotent_charge_endpoint
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conditions: []
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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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| Clear problem | Apply best practice, automate |
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| Complicated | Expert review, formal analysis |
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| Complex (emergent) | Probe with chaos engineering, observability |
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| Chaotic (incident) | Act first, stabilize, then sense |
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| Tech debt | Distinguish essential vs accidental |
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**기본값**: Most production distributed systems 매 Complex domain 매 산다 → SLO + chaos + observability + post-incident review.
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## 🔗 Graph
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- 부모: [[Systems Theory]] · [[Cybernetics Foundations|Cybernetics]]
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- 변형: [[Chaos Engineering]]
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- 응용: [[Distributed Systems]] · [[Microservices]] · [[SRE]]
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- Adjacent: [[Conceptual Integrity]] · [[Emergent Behavior]]
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## 🤖 LLM 활용
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**언제**: incident retrospective, architecture decision in distributed system, tech debt classification, organizational design, ML system behavior analysis.
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**언제 X**: simple CRUD app design, single-node algorithm, bounded local logic.
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## ❌ 안티패턴
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- **Best-practice in complex domain**: clear-domain solution 을 complex domain 에 강제.
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- **Ignoring accidental complexity**: 매 essential 처럼 취급 → tooling 의 미개선.
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- **Predicting emergent behavior**: complex system 의 detail prediction 시도 — probe 가 답.
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- **No feedback loops in design**: system dynamics 무시 → 매 surprise outage.
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## 🧪 검증 / 중복
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- Verified (Brooks "No Silver Bullet" / Snowden Cynefin / Mitchell "Complexity: A Guided Tour" / Burgess "Thinking in Promises").
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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 — Cynefin + Brooks essential/accidental + chaos engineering |
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