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

Márton Csillag1, Hamza Giaffar2, Eörs Szathmáry3

  • 1Institute of Evolution, Centre for Ecological Research, 1121 Budapest, Hungary.

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|December 21, 2024
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Summary
This summary is machine-generated.

This study demonstrates how selection for accurate Bayesian inference can drive the emergence of Darwinian evolution in representational units. This computational model explains how complex information is maintained and improved, potentially explaining brain evolution.

Keywords:
Bayesian inferenceDarwinian evolutionDarwinian neurodynamicsExaptationReplicator dynamicsTransition in individuality

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Area of Science:

  • Computational Neuroscience
  • Evolutionary Biology
  • Artificial Intelligence

Background:

  • Algorithmic equivalence exists between finite population replicator dynamics and Bayesian inference approximations.
  • Non-Darwinian processes, like noisy information transmission without heredity, can serve as a starting point.

Purpose of the Study:

  • To computationally model the emergence of Darwinian evolution in representational units.
  • To demonstrate how collective selection for Bayesian inference drives evolutionary adaptation.
  • To explore potential physical implementations of this evolutionary mechanism.

Main Methods:

  • Designed a computational model based on algorithmic equivalence.
  • Simulated collectives of units undergoing noisy, selection-dependent information transmission.
  • Applied collective-level selection for accurate Bayesian inference.

Main Results:

  • Demonstrated the emergence of Darwinian evolution within collectives of representational units.
  • Showcased the maintenance and iterative improvement of complex combinatorial information.
  • Identified a top-down transition in individuality when collectives are Darwinian.

Conclusions:

  • Selection for Bayesian inference can induce Darwinian evolution in representational systems.
  • This mechanism may explain the evolution of fast-timescale Darwinian dynamics in neural representations for planning.
  • Potential applications include prebiotic chemistry and reinforcement learning agents.