---
id: wiki-2026-0508-elite-theory
title: Elite Theory
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Power Elite, Iron Law of Oligarchy, Elitism]
duplicate_of: none
source_trust_level: A
confidence_score: 0.88
verification_status: applied
tags: [political-theory, sociology, governance, power]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python
  framework: NetworkX
---

# Elite Theory

## 매 한 줄
> **"매 모든 society 의 small minority 가 power 의 hold — 매 democracy 의 even"**. Pareto, Mosca, Michels (early 20C) 의 founded, C. Wright Mills (1956) 의 American extension, 매 modern network science / computational social science 의 quantification. 매 governance, AI alignment (concentration of model access), platform economy 의 lens.

## 매 핵심

### 매 founders
- **Vilfredo Pareto** (1916): "circulation of elites" — 매 lions (force) ↔ foxes (cunning) cycle.
- **Gaetano Mosca**: "ruling class" 매 inevitable; democracies are elites pretending otherwise.
- **Robert Michels** (1911): "iron law of oligarchy" — 매 organization 의 size 가 grow 면 oligarchy 의 emergent.
- **C. Wright Mills** (1956): "Power Elite" — military-corporate-political triangle.

### 매 mechanisms
- **Resource concentration**: capital, network ties, information access.
- **Coordination cost**: small group easier to organize than masses.
- **Self-perpetuation**: education credentials, marriage networks, board interlocks.
- **Cognitive capture**: regulators 매 industry insiders 의 share.

### 매 응용
1. Political science: explain policy capture.
2. Network analysis: detect elite clusters in citation/board graphs.
3. AI governance: model access, frontier lab concentration.
4. Platform economy: creator economy power-law.
5. Tech industry: VC-founder-board interlocks.

## 💻 패턴

### Detect elite clusters in network (k-core)
```python
import networkx as nx

def elite_kcore(G, k=10):
    return nx.k_core(G, k=k)

# Board interlock graph: nodes=people, edges=co-membership
elite = elite_kcore(board_graph, k=5)
print(f"Elite size: {len(elite)} / {len(board_graph)}")
```

### Power-law fit (wealth distribution)
```python
import powerlaw
import numpy as np

wealth = np.array([...])  # incomes
fit = powerlaw.Fit(wealth, discrete=False)
alpha = fit.power_law.alpha
xmin = fit.power_law.xmin
print(f"alpha={alpha:.2f}, xmin={xmin:.0f}")
# alpha ≈ 2.0-2.5 typical for top wealth
```

### Gini coefficient
```python
def gini(x):
    x = np.sort(x)
    n = len(x)
    cum = np.cumsum(x)
    return (2 * np.sum((np.arange(1, n+1)) * x) - (n+1) * cum[-1]) / (n * cum[-1])
```

### Eigenvector centrality (influence)
```python
centrality = nx.eigenvector_centrality(G, max_iter=1000)
top_elite = sorted(centrality.items(), key=lambda x: -x[1])[:20]
```

### Circulation of elites (agent-based)
```python
class Elite:
    def __init__(self, type_): self.type = type_  # 'lion' or 'fox'
    def step(self):
        if self.type == 'lion' and stress > 0.7:
            self.type = 'fox'  # Pareto cycle
```

### Power concentration metric
```python
def top1_share(values):
    sorted_v = sorted(values, reverse=True)
    return sum(sorted_v[:max(1, len(values)//100)]) / sum(values)
```

## 매 결정 기준
| 상황 | Lens |
|---|---|
| Policy outcomes | Power-elite (Mills) |
| Org behavior | Iron-law-of-oligarchy (Michels) |
| Long-run dynamics | Circulation (Pareto) |
| Quantitative analysis | Network + power-law tools |
| Counter: dispersed power | Pluralism (Dahl) |

**기본값**: Network analysis + Gini + power-law fit for empirical claims.

## 🔗 Graph
- 응용: [[Liquid-Democracy]]

## 🤖 LLM 활용
**언제**: literature synthesis on power dynamics, network construction from text data, generating hypotheses.
**언제 X**: causal inference (need DAGs + IV), historical claims (verify primary sources), normative judgment.

## ❌ 안티패턴
- **Conspiracy framing**: elite theory ≠ secret cabal; structural emergence.
- **Single-elite assumption**: multiple competing elites usually (military/corporate/cultural).
- **Ignoring counter-elites**: opposition movements are also elites in formation.
- **Static analysis**: elites circulate; snapshot misses dynamics.
- **Power-law overclaim**: many distributions are log-normal, not pure power-law (test with `powerlaw` package).

## 🧪 검증 / 중복
- Verified (Mills 1956, Michels 1911, Pareto Trattato di sociologia generale, Domhoff "Who Rules America" series).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — theory + computational methods (network/Gini/power-law) |
