  Cognitive biases are widespread in humans and animals alike, and can
sometimes be reinforced by social interactions. One prime bias in judgment and
decision-making is the human tendency to underestimate large quantities.
Previous research on social influence in estimation tasks has generally focused
on the impact of single estimates on individual and collective accuracy,
showing that randomly sharing estimates does not reduce the underestimation
bias. Here, we test a method of social information sharing that exploits the
known relationship between the true value and the level of underestimation, and
study if it can counteract the underestimation bias. We performed estimation
experiments in which participants had to estimate a series of quantities twice,
before and after receiving estimates from one or several group members. Our
purpose was threefold: to study (i) whether restructuring the sharing of social
information can reduce the underestimation bias, (ii) how the number of
estimates received affects the sensitivity to social influence and estimation
accuracy, and (iii) the mechanisms underlying the integration of multiple
estimates. Our restructuring of social interactions successfully countered the
underestimation bias. Moreover, we find that sharing more than one estimate
also reduces the underestimation bias. Underlying our results are a human
tendency to herd, to trust larger estimates than one's own more than smaller
estimates, and to follow disparate social information less. Using a
computational modelling approach, we demonstrate that these effects are indeed
key to explain the experimental results. Overall, our results show that
existing knowledge on biases can be used to dampen their negative effects and
boost judgment accuracy, paving the way for combating other cognitive biases
threatening collective systems.
