Literature DB >> 35535494

Conformist social learning leads to self-organised prevention against adverse bias in risky decision making.

Wataru Toyokawa1, Wolfgang Gaissmaier1,2.   

Abstract

Given the ubiquity of potentially adverse behavioural bias owing to myopic trial-and-error learning, it seems paradoxical that improvements in decision-making performance through conformist social learning, a process widely considered to be bias amplification, still prevail in animal collective behaviour. Here we show, through model analyses and large-scale interactive behavioural experiments with 585 human subjects, that conformist influence can indeed promote favourable risk taking in repeated experience-based decision making, even though many individuals are systematically biased towards adverse risk aversion. Although strong positive feedback conferred by copying the majority's behaviour could result in unfavourable informational cascades, our differential equation model of collective behavioural dynamics identified a key role for increasing exploration by negative feedback arising when a weak minority influence undermines the inherent behavioural bias. This 'collective behavioural rescue', emerging through coordination of positive and negative feedback, highlights a benefit of collective learning in a broader range of environmental conditions than previously assumed and resolves the ostensible paradox of adaptive collective behavioural flexibility under conformist influences.
© 2022, Toyokawa and Gaissmaier.

Entities:  

Keywords:  collective behaviour; computational biology; conformity; hot stove effect; human; physics of living systems; reinforcement learning; risky decision making; social learning; systems biology

Mesh:

Year:  2022        PMID: 35535494      PMCID: PMC9090329          DOI: 10.7554/eLife.75308

Source DB:  PubMed          Journal:  Elife        ISSN: 2050-084X            Impact factor:   8.713


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