teleo-codex/domains/ai-alignment/multiplex-network-framework-models-collective-intelligence-as-three-interacting-layers-cognition-physical-information.md

type	domain	secondary_domains	description	confidence	source	created
claim	ai-alignment	collective-intelligence	Collective intelligence emerges from interactions across cognition physical and information network layers with both intra-layer and inter-layer dynamics	experimental	Patterns/Cell Press 2024 review proposing multiplex network framework for AI-enhanced collective intelligence	2026-03-11

Multiplex network framework models collective intelligence as three interacting layers cognition physical information

Collective intelligence in AI-human systems can be modeled as a multiplex network with three distinct but interacting layers: cognition (mental models, knowledge, reasoning), physical (spatial proximity, embodied interaction), and information (communication channels, data flows). Each layer has intra-layer dynamics (connections within the layer) and inter-layer dynamics (how layers influence each other).

Nodes in this framework represent both humans (varying in surface-level and deep-level diversity) and AI agents (varying in functionality and anthropomorphism). Collective intelligence emerges through both bottom-up processes (aggregation of individual contributions) and top-down processes (norms, structures, coordination mechanisms).

The framework provides a structured way to analyze where AI integration enhances versus degrades collective intelligence: enhancements and degradations can be localized to specific layers and specific types of connections. For example, AI might enhance information layer connectivity while degrading physical layer social bonds.

Evidence

• Patterns/Cell Press 2024 review proposes multiplex network framework as organizing structure for AI-enhanced collective intelligence research
• Framework distinguishes three layers: cognition, physical, information
• Nodes = humans (with diversity attributes) + AI agents (with functionality/anthropomorphism attributes)
• Collective intelligence emerges through bottom-up (aggregation) and top-down (norms/structures) processes

Limitations

The review notes this is a proposed framework, not a validated model. The authors explicitly state there is "no comprehensive theoretical framework" explaining when AI-CI systems succeed or fail, suggesting this multiplex network model is a research direction rather than established theory.

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Relevant Notes:

• collective intelligence is a measurable property of group interaction structure not aggregated individual ability
• intelligence is a property of networks not individuals
• domains/ai-alignment/_map
• foundations/collective-intelligence/_map

Topics:

• foundations/collective-intelligence/_map
• domains/ai-alignment/_map