Meta-control of social learning strategies

Anil Yaman*, Nicolas Bredeche, Onur Caylak, Joel Z. Leibo, Sang Wan Lee

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

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.

Original languageEnglish
Article numbere1009882
Pages (from-to)1-27
Number of pages27
JournalPLoS Computational Biology
Volume18
Issue number2
DOIs
Publication statusPublished - 28 Feb 2022

Bibliographical note

Publisher Copyright:
© 2022 Yaman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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