English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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

Crowd control: Reducing individual estimation bias by sharing biased social information