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

• Published in: eLife Vol. 11
• Main Authors: Toyokawa, Wataru, Gaissmaier, Wolfgang
• Format: Journal Article
• Language: English
• Published: England eLife Sciences Publications Ltd 10.05.2022; eLife Sciences Publications, Ltd
• Subjects: Accuracy; Animals; Aversion; Behavior, Animal; Bias; collective behaviour; Computational and Systems Biology; conformity; Decision Making; Environmental conditions; Feedback; hot stove effect; Humans; Influence; Learning; Normal distribution; Parameter estimation; Physics of Living Systems; reinforcement learning; risky decision making; Social discrimination learning; Social Learning; computational biology; systems biology; risky decision making; social learning; collective behaviour; conformity; hot stove effect; human; physics of living systems; reinforcement learning
• ISSN: 2050-084X; 2050-084X
• DOI: 10.7554/eLife.75308

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. When it comes to making decisions, like choosing a restaurant or political candidate, most of us rely on limited information that is not accurate enough to find the best option. Considering others’ decisions and opinions can help us make smarter choices, a phenomenon called “collective intelligence”. Collective intelligence relies on individuals making unbiased decisions. If individuals are biased toward making poor choices over better ones, copying the group’s behavior may exaggerate biases. Humans are persistently biased. To avoid repeated failure, humans tend to avoid risky behavior. Instead, they often choose safer alternatives even when there might be a greater long-term benefit to risk-taking. This may hamper collective intelligence. Toyokawa and Gaissmaier show that learning from others helps humans make better decisions even when most people are biased toward risk aversion. The experiments first used computer modeling to assess the effect of individual bias on collective intelligence. Then, Toyokawa and Gaissmaier conducted an online investigation in which 185 people performed a task that involved choosing a safer or risker alternative, and 400 people completed the same task in groups of 2 to 8. The online experiment showed that participating in a group changed the learning dynamics to make information sampling less biased over time. This mitigated people’s tendency to be risk-averse when risk-taking is beneficial. The model and experiments help explain why humans have evolved to learn through social interactions. Social learning and the tendency of humans to conform to the group’s behavior mitigates individual risk aversion. Studies of the effect of bias on individual decision-making in other circumstances are needed. For example, would the same finding hold in the context of social media, which allows individuals to share unprecedented amounts of sometimes incorrect information?