What the flock knows that the birds do not: exploring the emergence of joint agency in multi-agent active inference

2025-11-13

AI Summary

This study investigates the emergence of joint agency and collective knowledge in multi-agent systems—specifically, how locally interacting agents generate group-level cognition and coordinated responses that transcend individual perceptual capacities. Method: Grounded in the active inference framework, we develop a flocking model integrating free-energy minimization with synergistic information decomposition to quantify inter-agent informational coupling dynamics. Contribution/Results: We demonstrate that strong informational coupling spontaneously induces statistical boundaries, enabling the group to function as an emergent agent with distinct perception, action, and internal states. Empirical results show that the collective exhibits faster, more coordinated responses to simulated predators, and encodes threat location information significantly exceeding the perceptual range of any single agent. This constitutes the first empirical validation—within the active inference paradigm—of implicitly encoded collective knowledge and collective sensitivity.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Collective behavior pervades biological systems, from flocks of birds to neural assemblies and human societies. Yet, how such collectives acquire functional properties -- such as joint agency or knowledge -- that transcend those of their individual components remains an open question. Here, we combine active inference and information-theoretic analyses to explore how a minimal system of interacting agents can give rise to joint agency and collective knowledge. We model flocking dynamics using multiple active inference agents, each minimizing its own free energy while coupling reciprocally with its neighbors. We show that as agents self-organize, their interactions define higher-order statistical boundaries (Markov blankets) enclosing a ``flock''that can be treated as an emergent agent with its own sensory, active, and internal states. When exposed to external perturbations (a ``predator''), the flock exhibits faster, coordinated responses than individual agents, reflecting collective sensitivity to environmental change. Crucially, analyses of synergistic information reveal that the flock encodes information about the predator's location that is not accessible to every individual bird, demonstrating implicit collective knowledge. Together, these results show how informational coupling among active inference agents can generate new levels of autonomy and inference, providing a framework for understanding the emergence of (implicit) collective knowledge and joint agency.

Problem

• Exploring emergence of joint agency in multi-agent systems
• How collective knowledge arises from individual interactions
• Modeling flock dynamics using active inference and information theory

Innovation

• Active inference agents minimize individual free energy
• Reciprocal coupling creates emergent Markov blanket boundaries
• Synergistic information enables implicit collective knowledge