Non-hierarchical communication networks model democratic participatory society

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Friedman associates the non-hierarchical communication network to the participatory dimension of a democratic society.claim0.856
  Interpretation that Friedman’s use of Baran’s distributed networks is a political statement for a non-paternalistic society.
• Connectionist models of cognition and learning identify conditions where collective intelligence can arise bottom-up, using only distributed learning mechanisms without system-level or global feedback.claim0.708
  Central claim about the power of connectionism.
• Hierarchical clique-structured networks can maintain multiscale coherence; biology evolved hierarchy and stigmergy as coherence solutions.claim0.703
• Hierarchical structure in interaction topology enables complex multiscale patterns that cannot exist in flat networks.claim0.700
  Explains why biological systems achieve organization across scales while language models struggle; grounds in free energy scaling
• Generative Adversarial Network (GAN)method0.698
  A self-supervised method where generator and discriminator compete; can lead to deceptive simulations.
• Feed-forward networksconcept0.697
  Networks without feedback loops, such as the Perceptron; limited to linearly separable functions.
• Foundation models trained on different data converge on similar latent representations, suggesting a Platonic form.claim0.695
• Hierarchical networksframework0.693
  Third model system studied; shown to support complex pattern formation unlike flat architectures.