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.

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

• 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.claim0.823
  Subclaim.
• MCA allows evolution to mask negative pleiotropic effects and explore adaptive space more freely.hypothesis0.811
  Homeostatic modules correct for mutations locally, enabling independent evolution of traits.
• The multiscale competency architecture (MCA) speeds evolutionary search by providing generalization, reliability, tractable search space, cryptic variation, and functional intermediates.claim0.806
  Main functional claim about MCA.
• MCA enables generalization: many genotypes map to the same functional phenotype, increasing robustness and speeding exploration.claim0.798
  Subclaim of how MCA speeds evolution.
• If intercellular signaling (not genes) is the cognitive medium of morphogenesis, then bioelectric signals can be read, interpreted, and rewritten to predictively control growth and form without genetic changes.hypothesis0.785
  Predictive hypothesis validated in planarians and other species; enables therapeutic manipulation of morphogenetic targets.
• Genetic information must encode not only physical forms but also complex self-modifying control mechanisms for behavior and development.claim0.775
• MCA provides functional intermediates: goal-directedness of modules pushes partial solutions toward attractors, resolving the problem of useful intermediates in evolution.claim0.774
  Subclaim.
• MCA generates cryptic variance: many mutations neutralized by developmental regulation persist as a hidden diversity pool exploitable in future environments.claim0.774
  Subclaim.