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Meta-control of social learning strategies

Yaman, Anil ; Bredeche, Nicolas ; Çaylak, Onur ; Leibo, Joel Z ; Lee, Sang Wan Traulsen, Arne

PLoS computational biology, 2022-02, Vol.18 (2), p.e1009882-e1009882 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
Meta-control of social learning strategies
Autor: Yaman, Anil ; Bredeche, Nicolas ; Çaylak, Onur ; Leibo, Joel Z ; Lee, Sang Wan
Traulsen, Arne
Assuntos: Artificial Intelligence ; Behavior ; Biology and Life Sciences ; Brain research ; Computer and Information Sciences ; Computer Science ; Control (Psychology) ; Copying ; Costs ; Grants ; Humans ; Knowledge acquisition ; Knowledge bases (artificial intelligence) ; Learning ; Learning strategies ; Machine Learning ; Multiagent Systems ; Neurosciences ; Physical Sciences ; Population ; Psychological research ; Research and Analysis Methods ; Social Behavior ; Social discrimination learning ; Social Learning ; Social Sciences ; Success ; Uncertainty
É parte de: PLoS computational biology, 2022-02, Vol.18 (2), p.e1009882-e1009882
Notas: new_version
ObjectType-Article-1
SourceType-Scholarly Journals-1
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Current address: Computer Science Department, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
The authors have declared that no competing interests exist.
Descrição: Social learning, copying other's behavior without actual experience, offers a cost-effective means of knowledge acquisition. However, it raises the fundamental question of which individuals have reliable information: successful individuals versus the majority. The former and the latter are known respectively as success-based and conformist social learning strategies. We show here that while the success-based strategy fully exploits the benign environment of low uncertainly, it fails in uncertain environments. On the other hand, the conformist strategy can effectively mitigate this adverse effect. Based on these findings, we hypothesized that meta-control of individual and social learning strategies provides effective and sample-efficient learning in volatile and uncertain environments. Simulations on a set of environments with various levels of volatility and uncertainty confirmed our hypothesis. The results imply that meta-control of social learning affords agents the leverage to resolve environmental uncertainty with minimal exploration cost, by exploiting others' learning as an external knowledge base.
Editor: United States: Public Library of Science
Idioma: Inglês

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Meta-control of social learning strategies

Yaman, Anil ; Bredeche, Nicolas ; Çaylak, Onur ; Leibo, Joel Z ; Lee, Sang Wan Traulsen, Arne

United States: Public Library of Science

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