---
id: wiki-2026-0508-emergence
title: Emergence
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Emergent Behavior, Emergent Phenomena, Weak Emergence, Strong Emergence]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [complexity, philosophy, systems, ai, multi-agent]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python
  framework: NumPy/Mesa
---

# Emergence

## 매 한 줄
> **"매 whole 의 properties 매 parts 의 sum 의 not derivable — 매 collective behavior 의 birth"**. Aristotle origin, Mill 1843 "heteropathic laws", Anderson 1972 "More is Different", modern formal version 매 Bedau (weak emergence) / Chalmers (strong). 매 LLM "emergent capabilities" 의 2022-26 controversy (Schaeffer 2023 reframed as metric artifact).

## 매 핵심

### 매 두 types
- **Weak emergence** (Bedau): macro patterns derivable from micro by simulation only — irreducible in practice, reducible in principle (e.g. Conway's Life, traffic jams, ant trails).
- **Strong emergence** (Chalmers): genuinely novel causal powers not reducible — controversial (consciousness, ?).
- **Nominal emergence**: just labeling (descriptive, weakest sense).

### 매 hallmarks
- Many simple parts + simple interactions.
- Macro pattern not present in any single part.
- Often power-law / scale-free statistics.
- Self-organization without central control.

### 매 응용
1. Multi-agent systems (swarms, markets, traffic).
2. Cellular automata (CA, Conway's Life).
3. LLM emergent capabilities debate (in-context learning, CoT).
4. Phase transitions in physics.
5. Consciousness research (IIT, GWT).
6. Biology (flocking, embryogenesis).

## 💻 패턴

### Conway's Game of Life (canonical emergence)
```python
import numpy as np

def step(grid):
    nb = sum(np.roll(np.roll(grid, i, 0), j, 1)
             for i in (-1, 0, 1) for j in (-1, 0, 1) if (i, j) != (0, 0))
    return ((nb == 3) | ((grid == 1) & (nb == 2))).astype(int)

grid = np.random.choice([0, 1], (50, 50), p=[0.7, 0.3])
for _ in range(100):
    grid = step(grid)
```

### Boids (flocking)
```python
def boids_step(pos, vel, n_neighbors=10):
    # alignment, cohesion, separation
    new_vel = vel.copy()
    for i in range(len(pos)):
        d = np.linalg.norm(pos - pos[i], axis=1)
        nb = np.argsort(d)[1:n_neighbors+1]
        align = vel[nb].mean(0) - vel[i]
        cohere = pos[nb].mean(0) - pos[i]
        sep = -(pos[nb] - pos[i]).sum(0) / (d[nb][:, None] + 1e-3).sum()
        new_vel[i] += 0.05*align + 0.01*cohere + 0.1*sep
    return new_vel
```

### Detect emergence (mutual information micro→macro)
```python
from sklearn.metrics import mutual_info_score

def emergence_score(micro_states, macro_states):
    # High macro→macro MI conditioning on past macro indicates emergence
    return mutual_info_score(macro_states[:-1], macro_states[1:])
```

### LLM emergent capability (per Schaeffer 2023)
```python
# Discontinuous metric (exact match) shows "emergence"
# Continuous metric (token-level prob) shows smooth scaling
def reframe_emergence(model_sizes, exact_match, token_logp):
    # Plot both: token_logp is smooth, exact-match has sharp jump
    return {"continuous": token_logp, "discrete": exact_match}
```

### Cellular automaton (1D, Wolfram Class 4)
```python
def ca_1d(rule, n_cells=200, n_steps=200):
    rule_bin = [(rule >> i) & 1 for i in range(8)]
    state = np.zeros(n_cells, dtype=int); state[n_cells//2] = 1
    history = [state.copy()]
    for _ in range(n_steps):
        nb = state[:-2]*4 + state[1:-1]*2 + state[2:]
        state[1:-1] = [rule_bin[n] for n in nb]
        history.append(state.copy())
    return np.array(history)
ca_1d(110)  # Class 4 — emergent gliders, Turing-complete
```

### Phase transition (Ising model)
```python
def ising_step(spins, beta):
    i, j = np.random.randint(0, spins.shape[0], 2)
    nb = (spins[(i+1)%N, j] + spins[(i-1)%N, j] +
          spins[i, (j+1)%N] + spins[i, (j-1)%N])
    dE = 2 * spins[i, j] * nb
    if dE < 0 or np.random.rand() < np.exp(-beta * dE):
        spins[i, j] *= -1
    return spins
```

## 매 결정 기준
| 상황 | Frame |
|---|---|
| Multi-agent simulation | Weak emergence (Bedau) |
| LLM scaling capabilities | Question metric smoothness first |
| Consciousness | Strong emergence (still controversial) |
| Phase transitions | Statistical mechanics (rigorous) |
| Org behavior | Emergent vs designed properties |

**기본값**: weak emergence; demand operational definition + measurement.

## 🔗 Graph
- 부모: [[Complexity_Theory|Complexity-Theory]] · [[Systems-Theory]]
- 변형: [[Weak-Emergence]] · [[Strong-Emergence]] · [[Self-Organization]]
- 응용: [[Multi-agent-System|Multi-Agent-Systems]] · [[Cellular-Automata]] · [[LLM-Scaling]]
- Adjacent: [[Global-Neuronal-Workspace]]

## 🤖 LLM 활용
**언제**: simulating CA / boids / Ising, explaining emergence intuitions, critiquing claims of "emergence" (Schaeffer-style metric scrutiny).
**언제 X**: claims of strong emergence (philosophically contested), consciousness assertions (active research).

## ❌ 안티패턴
- **Emergence as magic**: "the system has emergent properties" 의 explanation 의 not.
- **Confusing weak with strong**: most "emergent" is weak (Conway's Life is weak).
- **Metric artifact emergence**: discontinuous evaluation creating apparent jumps (Schaeffer 2023).
- **Skipping operational definition**: define what is "emerging" measurably.
- **Claiming irreducibility prematurely**: lack of current explanation ≠ in-principle irreducibility.

## 🧪 검증 / 중복
- Verified (Anderson 1972 Science, Bedau "Weak Emergence" 1997, Chalmers "Strong and Weak Emergence" 2006, Schaeffer et al. NeurIPS 2023 "Are Emergent Abilities a Mirage?").
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — weak/strong emergence + CA/boids/Ising + Schaeffer LLM critique |
