Kurvers Ralf H J M, Herzog Stefan M, Hertwig Ralph, Krause Jens, Wolf Max
Center for Adaptive Rationality, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany.
Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587 Berlin, Germany.
iScience. 2021 Jun 17;24(7):102740. doi: 10.1016/j.isci.2021.102740. eCollection 2021 Jul 23.
Decision makers in contexts as diverse as medical, judicial, and political decision making are known to differ substantially in response bias and accuracy, and these differences are a major factor undermining the reliability and fairness of the respective decision systems. Using theoretical modeling and empirical testing across five domains, we show that collective systems based on pooling decisions robustly overcome this important but as of now unresolved problem of experts' heterogeneity. In breast and skin cancer diagnostics and fingerprint analysis, we find that pooling the decisions of five experts reduces the variation in sensitivity among decision makers by 52%, 54%, and 41%, respectively. Similar reductions are achieved for specificity and response bias, and in other domains. Thus, although outcomes in individual decision systems are highly variable and at the mercy of individual decision makers, collective systems based on pooling decrease this variation, thereby promoting reliability, fairness, and possibly even trust.
众所周知，在医学、司法和政治决策等各种不同背景下的决策者，在反应偏差和准确性方面存在很大差异，而这些差异是破坏各自决策系统可靠性和公平性的一个主要因素。通过对五个领域的理论建模和实证检验，我们表明，基于汇总决策的集体系统有力地克服了专家异质性这一重要但迄今尚未解决的问题。在乳腺癌和皮肤癌诊断以及指纹分析中，我们发现汇总五位专家的决策分别将决策者之间的敏感性差异降低了52%、54%和41%。在特异性和反应偏差以及其他领域也实现了类似的降低。因此，尽管个体决策系统的结果高度可变且受个体决策者的影响，但基于汇总的集体系统减少了这种变异性，从而提高了可靠性、公平性，甚至可能还有信任度。
