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