We hypothesise that ecological models fall short of demonstrating spontaneous evolution of a new level of individuality because they are single-level networks of symmetric interactions.

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

• The kind of relationships necessary to produce evolutionary individuality – the generation and heritability of fitness differences at the collective level – are the same as those required to produce organismic individuality – the information integration and collective action characteristic of a self.claim0.821
  Core conjecture linking evolutionary and organismic individuality.
• Evolutionary transitions in individuality correspond to deep interaction structures or perhaps other mechanisms of multi-scale dynamics.hypothesis0.811
  Concrete proposal about necessary architecture for ETIs.
• Evolutionary transitions in individuality constitute a form of deep model induction.claim0.808
  Links ETIs to the learning of hierarchical representations.
• The same learning behaviours can be exhibited by an ecological community without selection at the community level.claim0.807
  Extends connectionist learning to ecological networks.
• We hypothesize that evolutionary individuality, organismic individuality and cognition are coextensive.hypothesis0.799
  Strong unification proposal.
• Power's ecosystem model with individual-level selection on interaction traits evolves to solve Sudoku puzzles (Power 2019)finding0.796
  Demonstrates that ecological networks can learn complex problem-solving without system-level selection
• For a new level of individuality to have causal role as evolutionary unit, heritable fitness differences must be non-aggregative (not decomposable into lower-level differences).claim0.793
  Core claim: monotonic non-linearities are insufficient; evolutionary outcomes must change depending on context.
• Ecological community networks can evolve associative memory under individual-level selection (Power et al. 2015)finding0.792
  Shows that systems can learn without presupposing system-level unit of selection