Bayes and Darwin: How replicator populations implement Bayesian computations

Czégel, Dániel, Giaffar, Hamza, Tenenbaum, Joshua B, Szathmáry, Eörs (February 2022) Bayes and Darwin: How replicator populations implement Bayesian computations. BioEssays : news and reviews in molecular, cellular and developmental biology. e2100255. ISSN 1521-1878

Abstract

Bayesian learning theory and evolutionary theory both formalize adaptive competition dynamics in possibly high-dimensional, varying, and noisy environments. What do they have in common and how do they differ? In this paper, we discuss structural and dynamical analogies and their limits, both at a computational and an algorithmic-mechanical level. We point out mathematical equivalences between their basic dynamical equations, generalizing the isomorphism between Bayesian update and replicator dynamics. We discuss how these mechanisms provide analogous answers to the challenge of adapting to stochastically changing environments at multiple timescales. We elucidate an algorithmic equivalence between a sampling approximation, particle filters, and the Wright-Fisher model of population genetics. These equivalences suggest that the frequency distribution of types in replicator populations optimally encodes regularities of a stochastic environment to predict future environments, without invoking the known mechanisms of multilevel selection and evolvability. A unified view of the theories of learning and evolution comes in sight.

Item Type: Paper
Subjects: bioinformatics > computational biology > algorithms
bioinformatics > computational biology
evolution
organism description > animal behavior > learning
CSHL Authors:
Communities: CSHL labs > Koulakov lab
Depositing User: Sasha Luks-Morgan
Date: 25 February 2022
Date Deposited: 02 Mar 2022 15:12
Last Modified: 02 Mar 2022 15:12
URI: https://repository.cshl.edu/id/eprint/40534

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