Laboratorio de Neurociencia, Universidad Torcuato Di Tella.
National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina.
Psychol Sci. 2024 Aug;35(8):872-886. doi: 10.1177/09567976241252138. Epub 2024 Jun 12.
The aggregation of many lay judgments generates surprisingly accurate estimates. This phenomenon, called the "wisdom of crowds," has been demonstrated in domains such as medical decision-making and financial forecasting. Previous research identified two factors driving this effect: the accuracy of individual assessments and the diversity of opinions. Most available strategies to enhance the wisdom of crowds have focused on improving individual accuracy while neglecting the potential of increasing opinion diversity. Here, we study a complementary approach to reduce collective error by promoting erroneous divergent opinions. This strategy proposes to anchor half of the crowd to a small value and the other half to a large value before eliciting and averaging all estimates. Consistent with our mathematical modeling, four experiments ( = 1,362 adults) demonstrated that this method is effective for estimation and forecasting tasks. Beyond the practical implications, these findings offer new theoretical insights into the epistemic value of collective decision-making.
许多人主观判断的聚合会产生惊人准确的结果。这种现象被称为“群体智慧”,已经在医学决策和金融预测等领域得到了验证。之前的研究确定了两个推动这一效应的因素:个体评估的准确性和意见的多样性。大多数提高群体智慧的可用策略都侧重于提高个体的准确性,而忽略了增加意见多样性的潜力。在这里,我们研究了一种通过促进错误的发散性意见来降低集体错误的补充方法。该策略建议在得出并平均所有估计值之前,将一半的人群锚定在一个小值,另一半锚定在一个大值。与我们的数学模型一致,四项实验(n=1362 名成年人)表明,该方法对于估计和预测任务是有效的。除了实际意义之外,这些发现还为集体决策的认知价值提供了新的理论见解。