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 School of Biological Sciences > Publications |
| Depositing User: | Sasha Luks-Morgan |
| Date: | 25 February 2022 |
| Date Deposited: | 02 Mar 2022 15:12 |
| Last Modified: | 29 Feb 2024 19:51 |
| URI: | https://repository.cshl.edu/id/eprint/40534 |
Actions (login required)
 |
Administrator's edit/view item |
