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Antigravity Agent
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---
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id: wiki-2026-0508-collective-intelligence
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title: Collective Intelligence
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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: [집단 지성, swarm intelligence, wisdom of the crowd, prediction market, DAO, multi-agent]
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duplicate_of: none
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source_trust_level: B
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confidence_score: 0.85
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verification_status: applied
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tags: [collective-intelligence, swarm, multi-agent, emergence, prediction-market, dao, wikipedia, open-source]
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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: psychology / multi-agent
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applicable_to: [Multi-Agent AI, Crowdsourcing, DAO Design, Prediction Market]
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---
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# Collective Intelligence
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## 매 한 줄
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> **"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.
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## 매 핵심
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### Surowiecki's 4 conditions (Wisdom of Crowds 2004)
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1. **Diversity** of opinion.
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2. **Independence** (no peer pressure).
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3. **Decentralization** (local knowledge).
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4. **Aggregation** (mechanism to combine).
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→ 매 4 의 모두 의 satisfy 시 의 collective > individual.
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### 매 example
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- **Galton's ox** (1906): 매 800 fair-goer 의 median 의 actual.
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- **Wikipedia**: 매 millions of editor → 매 encyclopedic.
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- **Open source**: 매 Linux, 매 React, 매 Linus's Law ("many eyes shallow bug").
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- **Stack Overflow**: 매 answer voting.
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- **Prediction market**: 매 IEM, Manifold, Polymarket.
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- **Ant colony / bee swarm**: 매 biological.
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### 매 fail mode
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- **Group think** (Janis): 매 conformity.
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- **Information cascade**: 매 follow first.
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- **Echo chamber**: 매 filter bubble.
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- **Tyranny of majority**: 매 minority 의 silence.
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- **Gaming**: 매 manipulation.
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- **Centralization creep**: 매 power 의 concentrate.
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### 매 mechanism
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#### Voting
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- **Plurality**: 매 simple.
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- **Approval**: 매 multi-tick.
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- **Ranked-choice**: 매 preference.
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- **Quadratic**: 매 cost-quadratic vote.
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#### Prediction market
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- **Real-money**: 매 incentive 의 align.
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- **Play-money** (Manifold): 매 ethics OK.
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- **LMSR** (Logarithmic Market Scoring): 매 algorithmic market maker.
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#### Aggregation
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- **Mean / median**.
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- **Bradley-Terry** (Elo).
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- **PageRank-like**.
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- **Bayesian**.
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#### Deliberation
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- **Polis** (Taiwan): 매 public input + clustering.
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- **vTaiwan**.
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- **Liquid democracy**.
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### 매 modern AI 의 응용
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1. **Multi-agent LLM**: 매 debate, 매 verifier.
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2. **Society of Mind** (Minsky).
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3. **Mixture of Experts**.
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4. **Self-play** (AlphaZero).
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5. **Crowd RLHF**: 매 large-scale labeler.
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6. **Constitutional AI 의 jury**.
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### 매 응용
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#### Software development
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- **Code review**.
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- **RFC consensus**.
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- **Open source contribution**.
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- **DORA team metrics**.
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#### Governance
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- **DAO**: 매 token vote.
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- **Cooperatives**.
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- **Liquid democracy**.
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#### Forecasting
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- **Prediction market**.
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- **Superforecaster** (Tetlock).
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#### Crowdsourcing
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- **MTurk**: 매 microtask.
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- **Citizen science** (Foldit, Galaxy Zoo).
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- **Wikipedia**.
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## 💻 패턴 (응용 — multi-agent LLM, prediction)
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### Multi-agent debate (LLM)
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```python
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def multi_agent_debate(question, n_agents=3, rounds=2):
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agents = [LLM(persona=f'agent_{i}') for i in range(n_agents)]
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# 매 round 1: independent
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initial = [a.answer(question) for a in agents]
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# 매 rounds: refine with peer answers
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history = initial
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for r in range(rounds):
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new_answers = []
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for i, a in enumerate(agents):
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others = [history[j] for j in range(n_agents) if j != i]
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new = a.refine(question, own=history[i], peers=others)
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new_answers.append(new)
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history = new_answers
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# 매 aggregate
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return aggregate(history) # 매 majority / median / weighted
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```
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### Self-consistency (single model)
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```python
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def self_consistency(model, question, n=8):
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answers = [model.generate(question, temperature=0.7) for _ in range(n)]
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final_answers = [extract_final_answer(a) for a in answers]
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return Counter(final_answers).most_common(1)[0][0]
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```
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### Prediction market (LMSR)
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```python
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import math
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class LMSRMarket:
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"""매 Logarithmic Market Scoring Rule."""
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def __init__(self, n_outcomes, b=10):
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self.q = [0.0] * n_outcomes
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self.b = b # 매 liquidity
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def cost(self):
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return self.b * math.log(sum(math.exp(qi / self.b) for qi in self.q))
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def price(self, outcome):
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return math.exp(self.q[outcome] / self.b) / sum(math.exp(qi / self.b) for qi in self.q)
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def buy(self, outcome, shares):
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prev = self.cost()
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self.q[outcome] += shares
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return self.cost() - prev # 매 cost to buy
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```
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### Quadratic voting
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```python
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def quadratic_vote(voter_credits, choices):
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"""매 cost = vote² → 매 strong preference 의 cost ↑."""
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votes = {}
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for choice in choices:
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v = voter_credits[choice].get('vote_count', 0)
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cost = v ** 2
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if cost > voter_credits['budget']:
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raise ValueError('Over budget')
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votes[choice] = v
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voter_credits['budget'] -= cost
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return votes
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```
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### Bradley-Terry (pairwise → score)
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```python
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import numpy as np
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from sklearn.linear_model import LogisticRegression
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def bradley_terry(matches, n_items):
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X = np.zeros((len(matches), n_items))
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y = np.ones(len(matches))
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for i, (winner, loser) in enumerate(matches):
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X[i, winner] = 1
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X[i, loser] = -1
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clf = LogisticRegression(fit_intercept=False).fit(X, y)
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scores = clf.coef_[0]
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return scores # 매 LMSYS Arena 의 base
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```
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### Polis-style deliberation
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```python
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def cluster_opinions(statements, votes):
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"""매 vote matrix 의 cluster 의 group."""
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from sklearn.decomposition import PCA
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from sklearn.cluster import KMeans
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# 매 vote matrix: voter × statement (-1, 0, 1)
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pca = PCA(n_components=2).fit_transform(votes)
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clusters = KMeans(n_clusters=3).fit_predict(pca)
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# 매 매 cluster 의 representative statement
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consensus = []
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for c in set(clusters):
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cluster_voters = (clusters == c)
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# 매 매 cluster 가 매 같이 +1 의 statement
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statement_avg = votes[cluster_voters].mean(axis=0)
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top_statements = np.argsort(statement_avg)[-3:]
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consensus.append({'cluster': c, 'agree_on': [statements[s] for s in top_statements]})
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# 매 universal: 매 모든 cluster 가 +1
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universal = np.where((votes.mean(axis=0) > 0.5) & (votes.std(axis=0) < 0.3))[0]
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return {
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'clusters': consensus,
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'universal_agreement': [statements[s] for s in universal],
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}
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```
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### Superforecaster aggregation (Tetlock)
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```python
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def aggregate_forecasts(forecasts, weights=None):
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"""매 weighted geometric mean (Tetlock recommended)."""
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if weights is None:
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weights = [1] * len(forecasts)
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weights = np.array(weights) / sum(weights)
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# 매 logit transform → 매 weighted average → 매 inverse
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logits = [np.log(p / (1 - p)) for p in forecasts]
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weighted = sum(w * l for w, l in zip(weights, logits))
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return 1 / (1 + np.exp(-weighted))
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```
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## 🤔 결정 기준
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| 상황 | Mechanism |
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|---|---|
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| Numeric estimate | Median / mean of independents |
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| Forecasting | Prediction market or weighted forecaster |
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| Multi-criteria | Quadratic voting |
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| Pairwise | Bradley-Terry / Elo |
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| Deliberation | Polis / liquid democracy |
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| LLM accuracy | Self-consistency or multi-agent debate |
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| Code review | Required reviewer + LGTM |
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**기본값**: 매 4 condition 의 satisfy + 매 aggregation mechanism 의 explicit.
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## 🔗 Graph
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- 부모: [[Multi-agent-System|Multi-Agent-Systems]] · [[Decision Theory]]
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- 변형: [[Swarm_Intelligence|Swarm-Intelligence]] · [[Prediction-Market]]
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- 응용: [[DAO]]
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- AI 응용: [[Self-Consistency]] · [[Mixture-of-Experts]] · [[Best-of-N_Sampling]]
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- Adjacent: [[Cognitive Biases]] · [[Anarchism]] · [[Bounded_Rationality|Bounded-Rationality]] · [[Beliefs]]
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## 🤖 LLM 활용
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**언제**: 매 multi-agent design. 매 governance system. 매 forecasting. 매 crowdsource. 매 LLM accuracy boost.
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**언제 X**: 매 expert-only domain. 매 fast individual decision.
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## ❌ 안티패턴
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- **Group think**: 매 conformity.
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- **Information cascade**: 매 first vote 의 anchor.
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- **No diversity**: 매 single perspective.
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- **Centralized aggregation 의 manipulate**: 매 platform 의 power.
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- **Real-money market 의 ethics**: 매 medical / political.
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- **One-shot vote**: 매 deliberation X.
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## 🧪 검증 / 중복
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- Verified (Surowiecki "Wisdom of Crowds", Tetlock "Superforecasting", Galton 1906).
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- 신뢰도 B.
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- Related: [[Anarchism]] · [[Bounded_Rationality|Bounded-Rationality]] · [[Cognitive Biases]] · [[Best-of-N_Sampling]] · [[Multi-agent-System|Multi-Agent-Systems]].
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — Surowiecki 4 + 매 multi-agent debate / LMSR / quadratic vote / Bradley-Terry / Polis code |
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