MCA creates a more tractable search space by smoothing the fitness landscape, mitigating the inverse problem and enabling survival of mutations that would otherwise be deleterious.

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

• MCA allows evolution to mask negative pleiotropic effects and explore adaptive space more freely.hypothesis0.870
  Homeostatic modules correct for mutations locally, enabling independent evolution of traits.
• MCA enables generalization: many genotypes map to the same functional phenotype, increasing robustness and speeding exploration.claim0.855
  Subclaim of how MCA speeds evolution.
• MCA and its coarse-grained control layers (like bioelectric patterns) mitigate the inverse problem by providing a more linear relation between control signals and phenotypes.claim0.823
  Argues that intervening control layers decompose the genotype-phenotype mapping into two easier problems.
• MCA provides functional intermediates: goal-directedness of modules pushes partial solutions toward attractors, resolving the problem of useful intermediates in evolution.claim0.822
  Subclaim.
• MCA generates cryptic variance: many mutations neutralized by developmental regulation persist as a hidden diversity pool exploitable in future environments.claim0.815
  Subclaim.
• Evolution actually searches not only genotype space but a more tractable space of behavior-shaping signals that exploit cellular intelligence.claim0.812
  Central thesis of the paper.
• MCA facilitates credit assignment because error-correcting competencies of modules reduce the need to isolate individual gene contributions.claim0.807
  Claim about how MCA helps evolution deal with pleiotropy.
• Small independent lumps of problem-solving information are more likely to survive and spread into the gene pool.claim0.801
  Generalization from Holland's genetics insight to social and architectural sequences.