| Autor: |
Yaman, Anil, Bredeche, Nicolas, Çaylak, Onur, Leibo, Joel Z., Lee, Sang Wan |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
PLoS Comput Biol 18(2): e1009882 (2022) |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1371/journal.pcbi.1009882 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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