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As One And Many: Relating Individual And Emergent Group-level Generative Models In Active Inference.

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As One And Many: Relating Individual And Emergent Group-level Generative Models In Active Inference.

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As One and Many: Relating Individual and Emergent Group-Level Generative Models in Active Inference.

Peter Thestrup Waade¹, Christoffer Lundbak Olesen¹, Jonathan Ehrenreich Laursen²

¹Interacting Minds Centre, Aarhus University, 8000 Aarhus, Denmark.

Entropy (Basel, Switzerland)

|February 26, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Active inference models how groups form larger agents. This study introduces a method to link individual agent models to group behavior, revealing non-trivial relationships in self-organizing systems.

Keywords:

Markov blanket active inference cognitive modelling collective intelligence emergence free energy principle multi-scale predictive processing

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Area of Science:

Computational Neuroscience
Theoretical Biology
Artificial Intelligence

Background:

Active inference, grounded in the Free Energy Principle, offers a unified framework for behavior and self-maintenance across scales.
Emergent group-level agents can form from collectives of individual agents if they maintain a group-level Markov blanket.
Understanding the generative models of these emergent group agents is challenging, limiting research in multi-scale active inference.

Purpose of the Study:

To propose a data-driven methodology for characterizing the relationship between a group-level agent's generative model and the dynamics of its constituent individual agents.
To demonstrate this methodology using a computational cognitive modeling approach.
To explore the implications for understanding self-organizing systems and nested active inference agents.

Main Methods:

Utilizing computational cognitive modeling and computational psychiatry techniques.
Simulating a collective of agents with Markov blankets on a Multi-Armed Bandit task using the ActiveInference.jl library.
Employing sampling-based parameter estimation to infer the generative model of the group-level agent.

Main Results:

A non-trivial relationship was identified between the generative models of individual agents and the emergent group-level agent.
The proposed methodology successfully characterized the link between individual and collective agent models.
The findings hold even in a simplified Multi-Armed Bandit task setting.

Conclusions:

The developed methodology provides a novel way to study multi-scale active inference and emergent group behavior.
This approach can be extended to analyze nested active inference agents across various spatiotemporal scales.
Further research can apply this methodology to diverse self-organizing systems, from cellular collectives to human societies.