---
type: claim
domain: ai-alignment
description: "Agents maintain explicit individual-level beliefs about other agents' internal states through model factorisation, enabling strategic planning without centralized coordination"
confidence: experimental
source: "Ruiz-Serra et al., 'Factorised Active Inference for Strategic Multi-Agent Interactions' (AAMAS 2025)"
created: 2026-03-11
secondary_domains: [collective-intelligence]
---

# Factorised generative models enable decentralized Theory of Mind in multi-agent active inference systems

Active inference agents can maintain explicit, individual-level beliefs about the internal states of other agents through factorisation of the generative model. This enables each agent to perform strategic planning in a joint context without requiring centralized coordination or a global model of the system.

The factorisation approach operationalizes Theory of Mind within the active inference framework: each agent models not just the observable behavior of others, but their internal states—beliefs, preferences, and decision-making processes. This allows agents to anticipate others' actions based on inferred mental states rather than just observed patterns.

## Evidence

Ruiz-Serra et al. (2024) demonstrate this through:

1. **Factorised generative models**: Each agent maintains a separate model component for each other agent's internal state
2. **Strategic planning**: Agents use these beliefs about others' internal states for planning in iterated normal-form games
3. **Decentralized representation**: The multi-agent system is represented in a decentralized way—no agent needs a global view
4. **Game-theoretic validation**: The framework successfully navigates cooperative and non-cooperative strategic interactions in 2- and 3-player games

## Implications

This architecture provides a computational implementation of Theory of Mind that:

- Scales to multi-agent systems without centralized coordination
- Enables strategic reasoning about others' likely actions based on inferred beliefs
- Maintains individual agent autonomy while supporting coordination
- Provides a formal framework for modeling how agents model each other

The approach bridges active inference (a normative theory of intelligent behavior) with game theory (a normative theory of strategic interaction).

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Relevant Notes:
- [[AI alignment is a coordination problem not a technical problem]]
- [[intelligence is a property of networks not individuals]]
- [[coordination protocol design produces larger capability gains than model scaling because the same AI model performed 6x better with structured exploration than with human coaching on the same problem]]

Topics:
- [[domains/ai-alignment/_map]]
- [[foundations/collective-intelligence/_map]]