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dc.contributor.authorCzégel, Dániel
dc.contributor.authorGiaffar, Hamza
dc.contributor.authorTenenbaum, Joshua B
dc.contributor.authorSzathmáry, Eörs
dc.date.accessioned2023-04-04T16:24:22Z
dc.date.available2023-04-04T16:24:22Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/1721.1/150397
dc.description.abstractBayesian 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.en_US
dc.language.isoen
dc.publisherWileyen_US
dc.relation.isversionof10.1002/BIES.202100255en_US
dc.rightsCreative Commons Attribution NonCommercial License 4.0en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/en_US
dc.sourceWileyen_US
dc.titleBayes and Darwin: How replicator populations implement Bayesian computationsen_US
dc.typeArticleen_US
dc.identifier.citationCzégel, Dániel, Giaffar, Hamza, Tenenbaum, Joshua B and Szathmáry, Eörs. 2022. "Bayes and Darwin: How replicator populations implement Bayesian computations." BioEssays, 44 (4).
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.relation.journalBioEssaysen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2023-04-04T16:19:05Z
dspace.orderedauthorsCzégel, D; Giaffar, H; Tenenbaum, JB; Szathmáry, Een_US
dspace.date.submission2023-04-04T16:19:07Z
mit.journal.volume44en_US
mit.journal.issue4en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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