Reyna Valerie F, Hans Valerie P, Corbin Jonathan C, Yeh Ryan, Lin Kelvin, Royer Caisa
Human Neuroscience Institute, Department of Human Development, and Center for Behavioral Economics and Decision Research, Cornell University.
Cornell Law School, Cornell University.
Psychol Public Policy Law. 2015 Aug;21(3):280-294. doi: 10.1037/law0000048. Epub 2015 Jun 22.
Despite the importance of damage awards, juries are often at sea about the amounts that should be awarded, with widely differing awards for cases that seem comparable. We tested a new model of damage award decision making by systematically varying the size, context, and meaningfulness of numerical comparisons or anchors. As a result, we were able to elicit large differences in award amounts that replicated for 2 different cases. Although even arbitrary dollar amounts (unrelated to the cases) influenced the size of award judgments, the most consistent effects of numerical anchors were achieved when the amounts were meaningful in the sense that they conveyed the gist of numbers as small or large. Consistent with the model, the ordinal gist of the severity of plaintiff's damages and defendant's liability predicted damage awards, controlling for other factors such as motivation for the award-judgment task and perceived economic damages. Contrary to traditional dual-process approaches, numeracy and cognitive style (e.g., need for cognition and cognitive reflection) were not significant predictors of these numerical judgments, but they were associated with lower levels of variability once the gist of the judgments was taken into account. Implications for theory and policy are discussed.
尽管损害赔偿裁决很重要，但陪审团往往对应裁定的赔偿金额感到困惑，对于看似类似的案件，赔偿裁决差异很大。我们通过系统地改变数值比较或锚定的大小、背景和意义，测试了一种新的损害赔偿裁决决策模型。结果，我们能够在两种不同的案件中引发赔偿金额的巨大差异。虽然即使是任意的美元金额（与案件无关）也会影响赔偿裁决的大小，但当这些金额在传达数字大小的主旨方面具有意义时，数值锚定的最一致效果就会实现。与该模型一致，原告损害的严重程度和被告责任的顺序主旨预测了损害赔偿裁决，同时控制了其他因素，如裁决判断任务的动机和感知的经济损害。与传统的双过程方法相反，数学能力和认知风格（如认知需求和认知反思）并不是这些数值判断的重要预测因素，但一旦考虑到判断的主旨，它们与较低的变异性水平相关。讨论了对理论和政策的影响。
