Borg, J and Channon, AD (2012) Testing the Variability Selection Hypothesis: The Adoption of Social Learning in Increasingly Variable Environments. Artificial Life, 13. 317 -324. ISSN 1064-5462

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The variability selection hypothesis predicts the adoption of versatile behaviors and survival strategies, in response to increasingly variable environments. In hominin evolution the most apparent adaptation for versatility is the adoption of social learning. The hypothesis that social learning will be adopted over other learning strategies, such as individual learning, when individuals are faced with increasingly variable environments is tested here using a genetic algorithm with steady state selection and constant population size. Individuals, constituted of binary string genotypes and phenotypes, are evaluated on their ability to match a target binary string, nominally known as the environment, with success being measured by the Hamming distance between the phenotype and environment. The state of any given locus in the environment is determined by a sine wave, the frequency of which increases as the simulation progresses thus providing increasing environmental variability. Populations exhibiting combinations of genetic evolution, individual learning and social learning are tested, with the learning rates of both individual and social learning allowed to evolve. We show that increasingly variable environments are sufficient but not necessary to provide an evolutionary advantage to those populations exhibiting the extra-genetic learning strategies, with social learning being favored over individual learning when populations are allowed to explore both strategies simultaneously. We also introduce a more biologically realistic model that allows for population collapse, and show that here the prior adoption of individual learning is a prerequisite for the successful adoption of social learning in increasingly variable environments.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 11 Nov 2014 10:50
Last Modified: 17 Dec 2018 11:31

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