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An Active Inference Model of Collective Intelligence.

Rafael Kaufmann1, Pranav Gupta2, Jacob Taylor3,4

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Entropy (Basel, Switzerland)
|July 2, 2021
PubMed
Summary

This study models collective intelligence using Active Inference Formulation (AIF). Adding cognitive abilities like Theory of Mind and Goal Alignment to AIF agents enhances system performance and endogenous alignment, crucial for complex adaptive systems.

Keywords:
active inferenceagent-based modelcollective intelligencecomplex adaptive systemscomputational modelfree energy principlemultiscale systems

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Area of Science:

  • Computational Neuroscience
  • Complex Systems Science
  • Artificial Intelligence

Background:

  • Collective intelligence, where systems outperform individual components, is key in social and behavioral sciences.
  • Existing formal models lack mathematical descriptions linking local agent interactions to global collective behavior.
  • Active Inference Formulation (AIF) offers a framework for non-equilibrium steady-state systems.

Purpose of the Study:

  • To develop a minimal agent-based model simulating the relationship between local interactions and collective intelligence using AIF.
  • To explore how cognitive capabilities (Theory of Mind, Goal Alignment) influence collective intelligence in AIF agents.
  • To investigate endogenous emergence of alignment in agent interactions.

Main Methods:

  • Utilized the Active Inference Formulation (AIF) to create agent-based models.
  • Introduced stepwise cognitive enhancements: baseline AIF, Theory of Mind, Goal Alignment, and both.
  • Simulated interactions between agents with varying cognitive sophistication.

Main Results:

  • Stepwise increases in cognitive ability (Theory of Mind, Goal Alignment) improved system performance.
  • Cognitive enhancements facilitated alignment between agents' local and global optima.
  • Alignment emerged endogenously from agent dynamics, not external incentives or top-down control.

Conclusions:

  • Cognitive sophistication is a critical factor for enhancing collective intelligence in agent-based systems.
  • Endogenous alignment mechanisms are vital for collective intelligence, as demonstrated by the AIF models.
  • Findings offer insights into information-theoretic patterns supporting collective intelligence in diverse complex adaptive systems.