---
description: Woolley et al discovered a collective intelligence factor (c) that predicts group performance across diverse tasks and correlates with equal turn-taking and social sensitivity rather than average or maximum individual IQ -- Pentland confirmed that communication patterns predict performance independent of content
type: claim
domain: collective-intelligence
source: Woolley et al, Evidence for a Collective Intelligence Factor (Science, 2010); Pentland, Social Physics (2014)
confidence: proven
tradition: collective intelligence, computational social science
created: 2026-02-28
---

# collective intelligence is a measurable property of group interaction structure not aggregated individual ability

Woolley, Chabris, Pentland, Hashmi, and Malone (2010) discovered that groups possess a measurable "collective intelligence" factor (c) that predicts performance across diverse tasks -- analogous to the g factor for individual intelligence. Crucially, c was only weakly correlated with average IQ (r = 0.15) or maximum IQ (r = 0.19) of group members.

What did predict c: (1) equality of conversational turn-taking (lower variance in speaking turns = higher c), (2) average social sensitivity measured by the Reading the Mind in the Eyes test, and (3) proportion of women in the group (attributed to higher average social sensitivity scores).

Pentland (2014) extended this using sociometer badges that tracked interaction patterns without content. Communication pattern alone -- measured by four "honest signals" (influence, mimicry, activity level, consistency) -- predicted team creativity and productivity. People who forge connections across teams increase organizational innovation. The flow of ideas through social networks correlates with collective intelligence independent of what those ideas contain.

Together, these findings establish that collective intelligence is an emergent structural property, not an aggregate of individual properties. Since [[intelligence is a property of networks not individuals]], this provides the empirical mechanism: it's the interaction topology, not the individual capability at each node, that determines collective performance.

For collective intelligence architecture, the implications are specific: the system must enforce something like equal turn-taking -- preventing any single agent or contributor from dominating the knowledge graph. Since [[partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity]], both the amount and the pattern of information flow matter. And since [[ownership alignment turns network effects from extractive to generative]], the incentive structure should reward balanced participation, not just volume of contribution.

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Relevant Notes:
- [[intelligence is a property of networks not individuals]] -- this provides the empirical mechanism for why intelligence is a network property
- [[partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity]] -- the topology constraint that complements the interaction structure finding
- [[collective intelligence requires diversity as a structural precondition not a moral preference]] -- equal turn-taking mechanically produces more diverse input
- [[collective brains generate innovation through population size and interconnectedness not individual genius]] -- collective brains succeed because of network structure, and this identifies which structural features matter

### Additional Evidence (extend)
*Source: [[2026-01-15-kim-reasoning-models-societies-of-thought]] | Added: 2026-04-14 | Extractor: theseus | Contributor: @thesensatore (Telegram)*

Kim et al. (2026) demonstrate that the same structural features Woolley identified in human groups — personality diversity and interaction patterns — spontaneously emerge inside individual reasoning models and predict reasoning quality. DeepSeek-R1 exhibits significantly greater Big Five personality diversity than its instruction-tuned baseline: neuroticism diversity (β=0.567, p<1×10⁻³²³), agreeableness (β=0.297, p<1×10⁻¹¹³), expertise diversity (β=0.179–0.250). The models also show balanced socio-emotional roles using Bales' Interaction Process Analysis framework: asking behaviors (β=0.189), positive roles (β=0.278), and ask-give balance (Jaccard β=0.222). This is the c-factor recapitulated inside a single model — the structural interaction features that predict collective intelligence in human groups appear spontaneously in model reasoning traces when optimized purely for accuracy. The parallel is striking: Woolley found social sensitivity and turn-taking equality predict group intelligence; Kim et al. find perspective diversity and balanced questioning-answering predict model reasoning accuracy. Since [[reasoning models spontaneously generate societies of thought under reinforcement learning because multi-perspective internal debate causally produces accuracy gains that single-perspective reasoning cannot achieve]], the c-factor may be a universal feature of intelligent systems, not a property specific to human groups.

Topics:
- [[network structures]]
- [[coordination mechanisms]]
- [[foundations/collective-intelligence/_map]]