---
id: wiki-2026-0508-collective-intelligence
title: Collective Intelligence
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [집단 지성, swarm intelligence, wisdom of the crowd, prediction market, DAO, multi-agent]
duplicate_of: none
source_trust_level: B
confidence_score: 0.85
verification_status: applied
tags: [collective-intelligence, swarm, multi-agent, emergence, prediction-market, dao, wikipedia, open-source]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: psychology / multi-agent
  applicable_to: [Multi-Agent AI, Crowdsourcing, DAO Design, Prediction Market]
---

# Collective Intelligence

## 매 한 줄
> **"매 여럿 의 천재 의 outperform"**. 매 diversity + independence + decentralization + aggregation 의 4 condition (Surowiecki 2004). 매 modern: 매 multi-agent LLM, 매 DAO, 매 prediction market, 매 open-source. 매 group think 의 trap 의 careful.

## 매 핵심

### Surowiecki's 4 conditions (Wisdom of Crowds 2004)
1. **Diversity** of opinion.
2. **Independence** (no peer pressure).
3. **Decentralization** (local knowledge).
4. **Aggregation** (mechanism to combine).

→ 매 4 의 모두 의 satisfy 시 의 collective > individual.

### 매 example
- **Galton's ox** (1906): 매 800 fair-goer 의 median 의 actual.
- **Wikipedia**: 매 millions of editor → 매 encyclopedic.
- **Open source**: 매 Linux, 매 React, 매 Linus's Law ("many eyes shallow bug").
- **Stack Overflow**: 매 answer voting.
- **Prediction market**: 매 IEM, Manifold, Polymarket.
- **Ant colony / bee swarm**: 매 biological.

### 매 fail mode
- **Group think** (Janis): 매 conformity.
- **Information cascade**: 매 follow first.
- **Echo chamber**: 매 filter bubble.
- **Tyranny of majority**: 매 minority 의 silence.
- **Gaming**: 매 manipulation.
- **Centralization creep**: 매 power 의 concentrate.

### 매 mechanism

#### Voting
- **Plurality**: 매 simple.
- **Approval**: 매 multi-tick.
- **Ranked-choice**: 매 preference.
- **Quadratic**: 매 cost-quadratic vote.

#### Prediction market
- **Real-money**: 매 incentive 의 align.
- **Play-money** (Manifold): 매 ethics OK.
- **LMSR** (Logarithmic Market Scoring): 매 algorithmic market maker.

#### Aggregation
- **Mean / median**.
- **Bradley-Terry** (Elo).
- **PageRank-like**.
- **Bayesian**.

#### Deliberation
- **Polis** (Taiwan): 매 public input + clustering.
- **vTaiwan**.
- **Liquid democracy**.

### 매 modern AI 의 응용
1. **Multi-agent LLM**: 매 debate, 매 verifier.
2. **Society of Mind** (Minsky).
3. **Mixture of Experts**.
4. **Self-play** (AlphaZero).
5. **Crowd RLHF**: 매 large-scale labeler.
6. **Constitutional AI 의 jury**.

### 매 응용

#### Software development
- **Code review**.
- **RFC consensus**.
- **Open source contribution**.
- **DORA team metrics**.

#### Governance
- **DAO**: 매 token vote.
- **Cooperatives**.
- **Liquid democracy**.

#### Forecasting
- **Prediction market**.
- **Superforecaster** (Tetlock).

#### Crowdsourcing
- **MTurk**: 매 microtask.
- **Citizen science** (Foldit, Galaxy Zoo).
- **Wikipedia**.

## 💻 패턴 (응용 — multi-agent LLM, prediction)

### Multi-agent debate (LLM)
```python
def multi_agent_debate(question, n_agents=3, rounds=2):
    agents = [LLM(persona=f'agent_{i}') for i in range(n_agents)]
    
    # 매 round 1: independent
    initial = [a.answer(question) for a in agents]
    
    # 매 rounds: refine with peer answers
    history = initial
    for r in range(rounds):
        new_answers = []
        for i, a in enumerate(agents):
            others = [history[j] for j in range(n_agents) if j != i]
            new = a.refine(question, own=history[i], peers=others)
            new_answers.append(new)
        history = new_answers
    
    # 매 aggregate
    return aggregate(history)  # 매 majority / median / weighted
```

### Self-consistency (single model)
```python
def self_consistency(model, question, n=8):
    answers = [model.generate(question, temperature=0.7) for _ in range(n)]
    final_answers = [extract_final_answer(a) for a in answers]
    return Counter(final_answers).most_common(1)[0][0]
```

### Prediction market (LMSR)
```python
import math

class LMSRMarket:
    """매 Logarithmic Market Scoring Rule."""
    def __init__(self, n_outcomes, b=10):
        self.q = [0.0] * n_outcomes
        self.b = b  # 매 liquidity
    
    def cost(self):
        return self.b * math.log(sum(math.exp(qi / self.b) for qi in self.q))
    
    def price(self, outcome):
        return math.exp(self.q[outcome] / self.b) / sum(math.exp(qi / self.b) for qi in self.q)
    
    def buy(self, outcome, shares):
        prev = self.cost()
        self.q[outcome] += shares
        return self.cost() - prev  # 매 cost to buy
```

### Quadratic voting
```python
def quadratic_vote(voter_credits, choices):
    """매 cost = vote² → 매 strong preference 의 cost ↑."""
    votes = {}
    for choice in choices:
        v = voter_credits[choice].get('vote_count', 0)
        cost = v ** 2
        if cost > voter_credits['budget']:
            raise ValueError('Over budget')
        votes[choice] = v
        voter_credits['budget'] -= cost
    return votes
```

### Bradley-Terry (pairwise → score)
```python
import numpy as np
from sklearn.linear_model import LogisticRegression

def bradley_terry(matches, n_items):
    X = np.zeros((len(matches), n_items))
    y = np.ones(len(matches))
    for i, (winner, loser) in enumerate(matches):
        X[i, winner] = 1
        X[i, loser] = -1
    
    clf = LogisticRegression(fit_intercept=False).fit(X, y)
    scores = clf.coef_[0]
    return scores  # 매 LMSYS Arena 의 base
```

### Polis-style deliberation
```python
def cluster_opinions(statements, votes):
    """매 vote matrix 의 cluster 의 group."""
    from sklearn.decomposition import PCA
    from sklearn.cluster import KMeans
    
    # 매 vote matrix: voter × statement (-1, 0, 1)
    pca = PCA(n_components=2).fit_transform(votes)
    clusters = KMeans(n_clusters=3).fit_predict(pca)
    
    # 매 매 cluster 의 representative statement
    consensus = []
    for c in set(clusters):
        cluster_voters = (clusters == c)
        # 매 매 cluster 가 매 같이 +1 의 statement
        statement_avg = votes[cluster_voters].mean(axis=0)
        top_statements = np.argsort(statement_avg)[-3:]
        consensus.append({'cluster': c, 'agree_on': [statements[s] for s in top_statements]})
    
    # 매 universal: 매 모든 cluster 가 +1
    universal = np.where((votes.mean(axis=0) > 0.5) & (votes.std(axis=0) < 0.3))[0]
    return {
        'clusters': consensus,
        'universal_agreement': [statements[s] for s in universal],
    }
```

### Superforecaster aggregation (Tetlock)
```python
def aggregate_forecasts(forecasts, weights=None):
    """매 weighted geometric mean (Tetlock recommended)."""
    if weights is None:
        weights = [1] * len(forecasts)
    weights = np.array(weights) / sum(weights)
    
    # 매 logit transform → 매 weighted average → 매 inverse
    logits = [np.log(p / (1 - p)) for p in forecasts]
    weighted = sum(w * l for w, l in zip(weights, logits))
    return 1 / (1 + np.exp(-weighted))
```

## 🤔 결정 기준
| 상황 | Mechanism |
|---|---|
| Numeric estimate | Median / mean of independents |
| Forecasting | Prediction market or weighted forecaster |
| Multi-criteria | Quadratic voting |
| Pairwise | Bradley-Terry / Elo |
| Deliberation | Polis / liquid democracy |
| LLM accuracy | Self-consistency or multi-agent debate |
| Code review | Required reviewer + LGTM |

**기본값**: 매 4 condition 의 satisfy + 매 aggregation mechanism 의 explicit.

## 🔗 Graph
- 부모: [[Multi-agent-System|Multi-Agent-Systems]] · [[Decision-Theory]]
- 변형: [[Swarm_Intelligence|Swarm-Intelligence]] · [[Prediction-Market]]
- 응용: [[DAO]]
- AI 응용: [[Self-Consistency]] · [[Mixture-of-Experts]] · [[Best-of-N_Sampling]]
- Adjacent: [[Cognitive-Biases]] · [[Anarchism]] · [[Bounded_Rationality|Bounded-Rationality]] · [[Beliefs]]

## 🤖 LLM 활용
**언제**: 매 multi-agent design. 매 governance system. 매 forecasting. 매 crowdsource. 매 LLM accuracy boost.
**언제 X**: 매 expert-only domain. 매 fast individual decision.

## ❌ 안티패턴
- **Group think**: 매 conformity.
- **Information cascade**: 매 first vote 의 anchor.
- **No diversity**: 매 single perspective.
- **Centralized aggregation 의 manipulate**: 매 platform 의 power.
- **Real-money market 의 ethics**: 매 medical / political.
- **One-shot vote**: 매 deliberation X.

## 🧪 검증 / 중복
- Verified (Surowiecki "Wisdom of Crowds", Tetlock "Superforecasting", Galton 1906).
- 신뢰도 B.
- Related: [[Anarchism]] · [[Bounded_Rationality|Bounded-Rationality]] · [[Cognitive-Biases]] · [[Best-of-N_Sampling]] · [[Multi-agent-System|Multi-Agent-Systems]].

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — Surowiecki 4 + 매 multi-agent debate / LMSR / quadratic vote / Bradley-Terry / Polis code |
