Bottom-up incremental adjustment of relationships can increase system-level welfare in a manner functionally equivalent to distributed learning.

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

• Reinforcement learning acting on individual characteristics affecting their connections to others can result in dynamics that are equivalent to unsupervised learning at the system scale.claim0.767
  Key insight linking individual rewards to system-level learning.
• Bottom-up learning creates a non-decomposable whole (attractors that are non-linearly separable functions of the inputs and depend on the system’s own internal history), which means that credit assignment or reward at the level of individual parts becomes ineffective.claim0.766
  Explains how collective cognition becomes irreducible to parts.
• Connectionist models can exhibit learning bottom-up, without centralised control or an external teacher, and without any performance feedback applied at the system level.claim0.764
  Key property of distributed unsupervised learning.
• 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.claim0.751
  Central claim about the power of connectionism.
• Connectionist learning principles apply equally to evolutionary systems where variation and selection alter network organization, instantiating distributed learning at evolutionary timescales.hypothesis0.744
• Learning neural networks can enable 'chunking' and rescale problem-solving to higher organizational levels, a mechanism intrinsic to transitions in individuality.hypothesis0.731
• The same learning behaviours can be exhibited by an ecological community without selection at the community level.claim0.730
  Extends connectionist learning to ecological networks.
• Scaling intelligence via expansion of cognitive boundaries through inclusion of others' stress-reduction in one's own homeostatic loops.claim0.729
  Central thesis: expanding an agent's sensors and goals outward to include others' states creates bidirectional feedback loop that scales intelligence and increases compassion.