---
id: wiki-2026-0508-emergence-in-systems
title: Emergence in Systems
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Emergent Behavior, Collective Behavior, Weak Emergence, Strong Emergence]
duplicate_of: none
source_trust_level: A
confidence_score: 0.90
verification_status: applied
tags: [emergence, complexity, multi-agent, self-organization, LLM-emergent]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: mesa/numpy
---

# Emergence in Systems

## 매 한 줄
> **"매 macro-level patterns 매 micro-rules 만으로 매 predictable 하지 않게 발생"**. 매 1875 G.H. Lewes 의 용어 도입, 매 1972 Anderson *More is Different* 가 매 modern foundation. 매 2026 LLM emergent capabilities (in-context learning, reasoning chains), swarm robotics, market crashes, neural avalanches 의 핵심 framework.

## 매 핵심

### 매 weak vs strong
- **Weak emergence (Bedau)**: 매 micro-rule 으로 simulate 가능, 매 closed-form predict 어려움. 매 대부분의 과학 examples.
- **Strong emergence (Chalmers)**: 매 micro 로 deduce 불가, 매 new causal powers. 매 controversial — 매 consciousness debate.

### 매 핵심 mechanisms
- **Local interactions + nonlinearity**: 매 ant colony, Conway's GoL.
- **Phase transitions**: 매 critical density 매 traffic jam, 매 percolation.
- **Self-organized criticality (Bak)**: 매 sandpile, neural avalanches.
- **Stigmergy**: 매 environment-mediated coordination (pheromones).
- **Symmetry breaking**: 매 Turing patterns, 매 cell differentiation.

### 매 detection / measurement
- **Mutual information** between scales.
- **Effective complexity** (Gell-Mann).
- **Phi (Φ)** integrated information (IIT).
- **Coarse-grained predictability**: 매 micro vs macro forecast accuracy.
- **Emergent capability scaling curves** (LLM): 매 phase transition at parameter threshold.

### 매 응용
1. **LLM scaling**: 매 few-shot reasoning 매 ~10B params 에서 emerge (Wei 2022).
2. **Swarm robotics**: 매 simple drones → flock formation, 매 task allocation.
3. **Market microstructure**: 매 HFT bots → flash crashes, 매 emergent volatility.
4. **Neural networks**: 매 grokking phenomenon, 매 induction heads emerge.

## 💻 패턴

### Conway's Game of Life (Classic)
```python
import numpy as np
from scipy.ndimage import convolve

def step(grid):
    kernel = np.array([[1,1,1],[1,0,1],[1,1,1]])
    nb = convolve(grid, kernel, mode='wrap')
    return ((nb == 3) | ((grid == 1) & (nb == 2))).astype(int)
```

### Boids (Flocking)
```python
class Boids:
    def __init__(self, n=200):
        self.pos = np.random.rand(n, 2) * 100
        self.vel = (np.random.rand(n, 2) - 0.5) * 2
    def step(self):
        # cohesion + separation + alignment
        for i in range(len(self.pos)):
            d = np.linalg.norm(self.pos - self.pos[i], axis=1)
            mask = (d > 0) & (d < 10)
            if mask.any():
                cohesion = (self.pos[mask].mean(0) - self.pos[i]) * 0.01
                alignment = (self.vel[mask].mean(0) - self.vel[i]) * 0.05
                close = (d > 0) & (d < 3)
                separation = -((self.pos[close] - self.pos[i]).sum(0)) * 0.1 if close.any() else 0
                self.vel[i] += cohesion + alignment + separation
        speed = np.linalg.norm(self.vel, axis=1, keepdims=True).clip(min=0.5, max=3)
        self.vel = self.vel / np.linalg.norm(self.vel, axis=1, keepdims=True) * speed
        self.pos = (self.pos + self.vel) % 100
```

### Sandpile (Self-Organized Criticality)
```python
def sandpile_step(grid, threshold=4):
    drops = grid >= threshold
    while drops.any():
        grid[drops] -= threshold
        # spread to 4 neighbors
        grid[1:] += np.roll(drops, -1, axis=0)[1:]
        # ... (similar for other neighbors)
        drops = grid >= threshold
    return grid  # avalanche size distribution → power law
```

### Schelling Segregation
```python
def schelling(grid, tolerance=0.3, iters=1000):
    n = grid.shape[0]
    for _ in range(iters):
        unhappy = []
        for i, j in np.ndindex(grid.shape):
            if grid[i,j] == 0: continue
            nb = grid[max(0,i-1):i+2, max(0,j-1):j+2].flatten()
            similar = (nb == grid[i,j]).sum() - 1
            if similar / max(1, (nb != 0).sum() - 1) < tolerance:
                unhappy.append((i,j))
        # swap unhappy with random empty
        ...
    return grid  # macroscopic segregation emerges from mild micro-preference
```

### LLM Emergent Capability Detector
```python
def emergent_capability_curve(scales, accuracies, threshold=0.5):
    """Find parameter scale where accuracy phase-transitions above random."""
    for s, a in zip(scales, accuracies):
        if a > threshold:
            return s
    return None
# Wei et al 2022 — abrupt jump for arithmetic, multi-step reasoning
```

### Mutual Information Across Scales
```python
from sklearn.feature_selection import mutual_info_regression
def emergence_index(micro, macro):
    """High MI(macro_t+1 | macro_t) - MI(macro_t+1 | micro_t) suggests emergence."""
    mi_macro = mutual_info_regression(macro[:-1].reshape(-1,1), macro[1:])[0]
    mi_micro = mutual_info_regression(micro[:-1], macro[1:])[0]
    return mi_macro - mi_micro
```

## 매 결정 기준
| 시스템 | Framework |
|---|---|
| Cellular automaton | GoL, ECA Wolfram class |
| Multi-agent RL | swarm intelligence, MARL |
| Physical phase transition | renorm group, Ising |
| Neural network capabilities | scaling laws, mech interp |
| Economic systems | ABM (Agent-Based Models) |
| Brain dynamics | criticality, neural avalanche |

**기본값**: 매 ABM with 매 minimal local rules → 매 observe macro pattern → 매 measure emergence index.

## 🔗 Graph
- 부모: [[Complexity Science]] · [[Systems Theory]]
- 변형: [[Weak-Emergence]] · [[Strong-Emergence]] · [[Self-Organization]]
- 응용: [[Swarm_Intelligence|Swarm-Intelligence]]
- Adjacent: [[Dissipative-Structures]] · [[Emergence]]

## 🤖 LLM 활용
**언제**: 매 multi-agent prompt 의 collective behavior 분석, 매 capability scaling 예측.
**언제 X**: 매 simple linear systems — 매 emergence framework overhead.

## ❌ 안티패턴
- **"emergent" 의 mystification**: 매 simply "I don't understand" 의 placeholder.
- **Strong emergence claim 남발**: 매 weak emergence 가 거의 모든 과학 case 에 충분.
- **Ignoring scale separation**: 매 macro 가 micro 의 평균이면 매 trivial — 매 nonlinearity 필요.
- **Mistaking correlation for emergence**: 매 둘 다 환경 forcing 으로 driven 가능.

## 🧪 검증 / 중복
- Verified (Anderson 1972 *More is Different*; Bedau 1997; Wei et al. 2022 *Emergent Abilities of LLMs*; Mitchell 2009 *Complexity*).
- 신뢰도 A.
- 매 사촌 페이지: [[Emergence]] (broader philosophical treatment).

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — full content with GoL, Boids, sandpile, Schelling, LLM emergent |
