群体控制：通过分享有偏见的社会信息来减少个人估计偏差
Crowd control: Reducing individual estimation bias by sharing biased social information
摘要：
认知偏见在人类和动物中普遍存在，有时会通过社会互动得到加强。判断和决策中的一个主要偏见是人类倾向于低估大量。以往关于估计任务中社会影响的研究一般集中在单个估计对个体和集体准确性的影响上，表明随机共享估计并不能减少低估偏差。在这里，我们测试了一种社会信息共享的方法，该方法利用了真实价值和低估水平之间的已知关系，并研究它是否可以抵消低估偏差。我们进行了估计实验，参与者必须估计一系列数量两次，之前和之后收到一个或几个小组成员的估计。我们有三个目的：研究（i）重组社会信息共享是否可以减少低估偏差，（ii）收到的估计数如何影响对社会影响的敏感性和估计准确性，以及（iii）整合多个估计数的机制。我们对社会互动的重组成功地克服了低估偏见。此外，我们发现共享多个估计也会减少低估偏差。我们的研究结果的基础是人类倾向于羊群，相信比自己更大的估计比更小的估计，并遵循不同的社会信息less.Using计算建模方法，我们证明，这些影响确实是解释实验结果的关键。总的来说，我们的研究结果表明，现有的偏见知识可以用来抑制其负面影响，提高判断的准确性，为打击其他威胁集体系统的认知偏见铺平道路。
英文摘要：
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.
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