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Area of Science:

  • Evolutionary biology
  • Theoretical biology
  • Mathematical biology

Background:

  • Life's complexity arises from evolutionary transitions in individuality.
  • Previous work linked these transitions to learning theory but lacked mathematical formalization.

Purpose of the Study:

  • To provide a mathematical framework formalizing the analogy between evolutionary transitions and learning.
  • To connect multilevel selection and evolutionary individuality with Bayesian inference and model comparison.

Main Methods:

  • Utilizing replicator dynamics and Bayesian update theory.
  • Developing mathematical equivalences between hierarchical population evolution and Bayesian inference.
  • Applying Bayesian model comparison to understand evolutionary transitions.

Main Results:

  • Evolution in hierarchical populations under multilevel selection is equivalent to Bayesian inference in hierarchical Bayesian models.
  • Evolutionary transitions in individuality are equivalent to learning hierarchical model structures via Bayesian model comparison.

Conclusions:

  • Evolutionary complexification can be understood through a learning theory lens.
  • The depth of individuality hierarchies reflects integrated environmental data, mirroring learning complexity.