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From Bayes to Darwin: Evolutionary search as an exaptation from sampling-based Bayesian inference

2025-02Artigo científico Acesso aberto
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dc.contributor.authorCsillag, Márton
dc.contributor.authorGiaffar, Hamza
dc.contributor.authorSzathmáry, Eörs
dc.contributor.authorSantos, Mauro
dc.contributor.authorCzégel, Dániel
dc.date.accessioned2025-01-08T16:20:36Z
dc.date.available2025-01-08T16:20:36Z
dc.date.issued2025-02
dc.description.abstractBuilding on the algorithmic equivalence between finite population replicator dynamics and particle filtering based approximation of Bayesian inference, we design a computational model to demonstrate the emergence of Darwinian evolution over representational units when collectives of units are selected to infer statistics of high-dimensional combinatorial environments. The non-Darwinian starting point is two units undergoing a few cycles of noisy, selection-dependent information transmission, corresponding to a serial (one comparison per cycle), non-cumulative process without heredity. Selection for accurate Bayesian inference at the collective level induces an adaptive path to the emergence of Darwinian evolution within the collectives, capable of maintaining and iteratively improving upon complex combinatorial information. When collectives are themselves Darwinian, this mechanism amounts to a top-down (filial) transition in individuality. We suggest that such a selection mechanism can explain the hypothesized emergence of fast timescale Darwinian dynamics over a population of neural representations within animal and human brains, endowing them with combinatorial planning capabilities. Further possible physical implementations include prebiotic collectives of non-replicating molecules and reinforcement learning agents with parallel policy search.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.jtbi.2024.112032pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.5/97007
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationHungarian National Research, Development, and Innovation Office (NKFIH, grant KKP129848)pt_PT
dc.relationTempleton World Charity Foundation (grant TWCF0268)pt_PT
dc.relationMiniLife ERC Synergy Grant (101118938)pt_PT
dc.relationGrant PID2021-127107NB-I00 from the Ministerio de Ciencia e Innovación (Spain)pt_PT
dc.relationGrant 2021 SGR 00526 from Generalitat de Catalunya 2021 SGR 00526pt_PT
dc.relationistinguished Guest Scientists Fellowship Programme of the Hungarian Academy of Sciencespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/pt_PT
dc.titleFrom Bayes to Darwin: Evolutionary search as an exaptation from sampling-based Bayesian inferencept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.startPage112032pt_PT
oaire.citation.titleJournal of Theoretical Biologypt_PT
oaire.citation.volume599pt_PT
person.familyNameSantos
person.givenNameMauro
person.identifier.ciencia-id6A12-5321-627A
person.identifier.orcid0000-0002-6478-6570
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication215be051-54a0-45ec-b3d0-f76c7d3519fc
relation.isAuthorOfPublication.latestForDiscovery215be051-54a0-45ec-b3d0-f76c7d3519fc

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