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Equality and Freedom as Uncertainty in Groups.

Jesse Hoey1

  • 1David R. Cheriton School of Computer Science, Univeristy of Waterloo, Waterloo, ON N2L 3G1, Canada.

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|November 27, 2021
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Summary
This summary is machine-generated.

This study links freedom to Bayesian model capacity, finding a "sweet spot" for group optimization. Optimal group function balances individual learning ease with collective adaptability, revealing a trade-off in model capacity.

Keywords:
POMDPactive inferenceaffect control theoryemotionfree energysociologyuncertainty

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Social dynamics

Background:

  • Bayesian hierarchical models are used to manage uncertainty in complex systems.
  • Group optimization involves agents aligning with collective and individual models.
  • Free energy minimization is a key principle in understanding system equilibria.

Purpose of the Study:

  • To investigate the connection between freedom and uncertainty management in Bayesian models.
  • To explore group optimization through decentralized agent alignment.
  • To identify optimal model capacities for group equilibria and social reasoning.

Main Methods:

  • Distributed factorization of group free energy optimization.
  • Analysis of decentralized optimization in agent models.
  • Development of a two-level Bayesian model for social reasoning.

Main Results:

  • A

Conclusions:

  • A correspondence exists between model capacity and freedom, where higher capacity allows interaction with more datasets.
  • Optimal group function requires balancing individual learning constraints with collective integration needs.
  • Three parameters of capacity in a dual-process Bayesian model relate to notions of freedom and equality.