MCA provides functional intermediates: goal-directedness of modules pushes partial solutions toward attractors, resolving the problem of useful intermediates in evolution.

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.claim0.822
  Subclaim.
• MCA enables generalization: many genotypes map to the same functional phenotype, increasing robustness and speeding exploration.claim0.802
  Subclaim of how MCA speeds evolution.
• MCA allows evolution to mask negative pleiotropic effects and explore adaptive space more freely.hypothesis0.784
  Homeostatic modules correct for mutations locally, enabling independent evolution of traits.
• MCA facilitates credit assignment because error-correcting competencies of modules reduce the need to isolate individual gene contributions.claim0.783
  Claim about how MCA helps evolution deal with pleiotropy.
• 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.claim0.774
  Argues that intervening control layers decompose the genotype-phenotype mapping into two easier problems.
• When a subset of cells is given a morphogenetic goal (e.g., build an eye), they autonomously recruit unmodified neighbors to complete the structure, showing modular competency.claim0.767
  Principle of top-down control.
• The large-scale morphological goals of an organism override and harness the local competencies of individual cells to adaptively navigate morphospace.claim0.764
  Describes top-down control in morphogenesis.
• MCA provides reliability: modular agential systems can be trusted to accomplish tasks even when changes occur, facilitating cooperation and investment in complex regulation.claim0.761
  Subclaim.