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 links	experimental	Patterns/Cell Press 2024 review proposing multiplex network framework	2026-03-11

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

The multiplex network framework models collective intelligence systems as three interacting network layers:

1. Cognition layer: Mental models, beliefs, knowledge structures
2. Physical layer: Face-to-face interactions, spatial proximity, physical infrastructure
3. Information layer: Digital communication, data flows, algorithmic connections

Each layer has its own network structure (nodes and edges), and collective intelligence emerges from both intra-layer dynamics (within each network) and inter-layer interactions (how the three networks influence each other).

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

Evidence

• Framework proposed in comprehensive review as synthesis of existing research
• Integrates findings from network science, organizational behavior, and AI-human collaboration studies
• Provides structure for analyzing when AI enhances vs. degrades collective intelligence
• The review identifies this as a key conceptual framework but notes it is descriptive rather than predictive

Framework Limitations

The review explicitly notes that this framework is descriptive, not predictive. It provides a way to categorize and analyze collective intelligence systems but does not yet predict when specific configurations will succeed or fail. The authors identify the lack of a "comprehensive theoretical framework" as a major gap in the field.

Relationship to Existing Work

This framework provides a formal structure for claims like collective intelligence is a measurable property of group interaction structure not aggregated individual ability by explicitly modeling the interaction structure across three network layers. It also connects to intelligence is a property of networks not individuals by treating collective intelligence as an emergent property of multiplex network dynamics.

---

Relevant Notes:

• collective intelligence is a measurable property of group interaction structure not aggregated individual ability
• intelligence is a property of networks not individuals
• partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity