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• W4379014778 abstract "The emergence of self-replication in chemical space has led to an explosive diversification of form and function. It is hypothesized that a similar process underlies human action selection in complex combinatorial spaces, such as the space of simulated action sequences. Furthermore, the spontaneous appearance of a non-predesigned evolutionary search in artificial agents might lead to a higher degree of open-endedness, arguably a key missing component of current machine intelligence. In this paper we design a computational model to show that Darwinian evolutionary dynamics over informational units can emerge if collectives of such units need to infer statistics of changing environments. We build our argument on a series of equivalences between Bayesian computations and replicator dynamics to demonstrate that the selective advantage of higher information transmission ability between units and of larger population size appear very early on, already at no consistently shared information between the population size of two units. Further selection for statistical inference at the collective level leads to a continuous increase of transmission fidelity and population size until the population reaches the ability to maintain and iteratively improve combinatorial information, a transition to the regime of Darwinian evolution. Candidate systems include prebiotic collectives of non-replicating molecules, collectives of neural ensembles representing competing action plans, and reinforcement learning agents with parallel policy search." @default.
• W4379014778 created "2023-06-02" @default.
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• W4379014778 date "2023-06-01" @default.
• W4379014778 modified "2023-09-25" @default.
• W4379014778 title "Emergence of Darwinian evolution in Bayesian collectives" @default.
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• W4379014778 doi "https://doi.org/10.1101/2023.05.29.542733" @default.
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