Bayes and Darwin: How replicator populations implement Bayesian computations
Name
BioEssays - 2022 - Cz gel - Bayes and Darwin How replicator populations implement Bayesian computations.pdf
Description
Published version
Size
1000.21 KB
Format
Adobe PDF
Checksum (MD5)
0d497e85136dc2fd207fd9335e25fce4
Author(s)
Czégel, Dániel
Giaffar, Hamza
Tenenbaum, Joshua B
Szathmáry, Eörs
Date Issued
2022
Journal
BioEssays
Publisher
Wiley
Citation
Czégel, Dániel, Giaffar, Hamza, Tenenbaum, Joshua B and Szathmáry, Eörs. 2022. "Bayes and Darwin: How replicator populations implement Bayesian computations." BioEssays, 44 (4).
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1002/BIES.202100255
