Meta-control of social learning strategies

TL;DR

This paper investigates how meta-control of social learning strategies, specifically success-based and conformist approaches, can improve learning efficiency in uncertain and volatile environments through simulations.

Contribution

It introduces the concept of meta-control of social learning strategies and demonstrates its effectiveness in uncertain environments via simulation results.

Findings

Success-based strategies excel in low uncertainty environments.

Conformist strategies mitigate adverse effects in high uncertainty.

Meta-control enhances learning efficiency in volatile environments.

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.