Antigravity Agent
162 lines
5.4 KiB
Markdown
162 lines
5.4 KiB
Markdown
---
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id: wiki-2026-0508-complex-systems
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title: Complex Systems
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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: [Complexity Theory, Complex Adaptive Systems, CAS]
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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, complexity, emergence, distributed-systems]
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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: n/a
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framework: n/a
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---
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# Complex Systems
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## 매 한 줄
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> **"매 Complex System 매 part 의 sum 초과 의 emergent 결과 발생 system"**. 매 simple-rule 매 unpredictable global 의 야기. Santa Fe Institute (Holland, Kauffman, Mitchell) 의 lineage. 2026 매 LLM swarm, distributed micro-services, social platform 매 canonical 예.
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## 매 핵심
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### 매 정의 specifics
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- **Many components** (10² ~ 10⁹).
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- **Local interaction** (no central control).
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- **Non-linearity**: 매 input → output 의 disproportionate.
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- **Emergence**: 매 macro behavior 매 micro rule 의 not directly inferrable.
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- **Adaptation**: 매 component 의 state-update 의 environment 응답.
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### 매 simple ↔ complicated ↔ complex (Cynefin)
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- **Simple**: 매 cause↔effect obvious. Best practice 의 사용.
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- **Complicated**: 매 expert analysis required. Good practice.
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- **Complex**: 매 retrospect 만 cause 추론 가능. 매 probe-sense-respond.
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- **Chaotic**: 매 cause↔effect link absent. Act-sense-respond.
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### 매 응용
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1. Distributed system design 매 emergent failure mode 의 anticipate.
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2. Org change 매 directly-controllable lever 부재 — 매 nudge.
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3. Market / social media 의 non-linear viral propagation.
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## 💻 패턴
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### Power-law detection (Pareto)
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```python
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import numpy as np, scipy.stats as st
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def is_powerlaw(data: np.ndarray) -> bool:
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"""Heavy-tailed → likely complex, not Gaussian."""
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fit = st.powerlaw.fit(data)
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ks_p = st.kstest(data, "powerlaw", fit).pvalue
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return ks_p > 0.05
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```
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### Agent-based model (Mesa)
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```python
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from mesa import Agent, Model
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from mesa.space import MultiGrid
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from mesa.time import RandomActivation
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class Cell(Agent):
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def step(self):
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n = self.neighbors_alive()
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self.alive = (n == 3) or (self.alive and n == 2)
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class Life(Model):
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def __init__(self, w=80, h=80):
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self.grid = MultiGrid(w, h, torus=True)
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self.schedule = RandomActivation(self)
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for x in range(w):
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for y in range(h):
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a = Cell(self)
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self.grid.place_agent(a, (x, y))
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self.schedule.add(a)
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```
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### Feedback-loop diagram (Mermaid)
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```mermaid
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graph LR
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Demand --> Price
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Price -->|+| Supply
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Supply -->|-| Price
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Price -->|-| Demand
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```
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### Tipping-point detection
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```python
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def early_warning_signal(timeseries):
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"""Increased variance + autocorrelation → near phase transition."""
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rolling_var = pd.Series(timeseries).rolling(50).var()
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rolling_ac = pd.Series(timeseries).rolling(50).apply(lambda x: x.autocorr(1))
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return rolling_var.iloc[-1] > rolling_var.mean() * 1.5 \
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and rolling_ac.iloc[-1] > 0.7
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```
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### Causal-loop policy lever map
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```yaml
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# policy_levers.yml
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goal: reduce-incident-rate
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levers:
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- lever: deploy-frequency
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feedback: positive # more deploys → more incidents short-term
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horizon: weeks
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- lever: test-coverage
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feedback: negative # higher coverage → fewer incidents
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horizon: months
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- lever: oncall-rotation-size
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feedback: negative # larger rotation → less burnout → fewer incidents
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horizon: quarters
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```
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### Network resilience metric
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```python
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import networkx as nx
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def fragility(G: nx.Graph) -> float:
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"""Higher = more fragile to targeted node removal."""
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bc = nx.betweenness_centrality(G)
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return max(bc.values()) - np.median(list(bc.values()))
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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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| Linear, well-understood | Optimization, KPI |
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| Complicated (expert solvable) | Plan + execute |
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| Complex (emergent) | Probe + small experiments + observe |
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| Chaotic (crisis) | Act first, stabilize, then sense |
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| Pre-tipping point | Early-warning + circuit-breaker |
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**기본값**: probe-sense-respond + diversity + redundancy.
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## 🔗 Graph
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- 부모: [[Systems Thinking]] · [[Cynefin Framework]]
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- 변형: [[Chaos Theory]] · [[Complex Adaptive Systems]]
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- 응용: [[Distributed Systems]] · [[Network Theory]]
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- Adjacent: [[Emergence]] · [[Power Laws]]
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## 🤖 LLM 활용
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**언제**: 매 system map 의 first-draft, 매 feedback-loop 의 surface, 매 policy lever brainstorm.
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**언제 X**: 매 prediction 의 complex system — 매 LLM 매 false confidence 매 위험. 매 historical analogy 의 limit.
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## ❌ 안티패턴
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- **Linear thinking**: 매 cause→effect 의 direct mapping 매 complex 에서 wrong.
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- **Optimization fallacy**: 매 single metric 의 optimization 매 emergent failure 야기 (Goodhart).
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- **Central control assumption**: 매 top-down command 매 local-rule system 매 ineffective.
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- **Reductionism over-reach**: 매 component 의 분석 매 emergent property 의 missing.
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- **Plan-the-future fallacy**: 매 5-year-plan 매 complex domain 매 fiction.
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## 🧪 검증 / 중복
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- Verified (Mitchell _Complexity: A Guided Tour_, Holland _Hidden Order_, Snowden Cynefin Framework, Santa Fe Institute lectures, Donella Meadows _Thinking in Systems_).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — Cynefin, agent-based model, power law, anti-patterns |
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