Quantitative Judgments in Law: Studies of Damage Award Decision Making

为了使社会运转良好，企业、保险公司和公众都需要一个可靠而称职的陪审团制度。在被告伤害原告且必须确定损害赔偿的民事案件中，民事陪审员必须将他们对原告伤害严重程度的主观定性判断转化为金钱。然而，许多伤害很难评估，民事陪审团得到的指导也很有限。一些陪审员在处理数字方面也有困难。本研究项目探讨如何改进陪审团损害赔偿的决策，使损害赔偿与所造成的伤害相匹配。该项目测试了一种新的损害赔偿决策模型的预测，该模型侧重于人们从审判证据中得出的要点或潜在意义。这些预测是：(a)大多数陪审员对损害赔偿的要点有一个连贯的认识；(b)基于要点的数值量级理解对于连贯的判断至关重要；(c)这种基于要点的理解可以通过简单的干预来促进陪审团的决策。提出了三个实验，以确定陪审团损害裁决的决策过程，并测试易于实施和成本效益的解决方案，以减少不必要的不一致。在许多商业和保险环境中出现了公平和一致的伤害评估问题，因此通过本研究发现的有效解决方案可能在法庭以外的其他领域被证明是有用的。法律制度经常要求事实发现者将他们的定性印象转化为定量判断，如刑事判决、罚款和损害赔偿。然而，法律中定性判断和定量判断之间的关系研究不足，理论化不足；目前还没有全面的理论来解释它。拟议的研究有望通过测试从损害赔偿决策的新模型中得出的具体预测，推动该领域的理论向前发展。这些实验将模糊追踪理论扩展到损害赔偿决策，测试了关于锚点（或基准）的意义和相对要点、相关和不相关的情感证据以及关于如何衡量损害的指示的效用的一些具体预测。具体来说，第一个实验调查了潜在陪审员如何解释案件事实的要点，这如何影响损害赔偿，以及提供有意义的锚定金额是否减少了赔偿金额的可变性。第二个实验的重点是相关和不相关情绪的影响，以及映射过程——简单的问题如何帮助潜在陪审员了解损害程度——目的是减少裁决的可变性。第三个实验在现实的模拟民事陪审团环境中测试了我们的新模型，包括陪审团审议。数据收集将包括计算能力（数字能力）的多种测量方法，使研究人员能够确定计算能力如何影响民事损害赔偿中有关美元的决策。

To have a well-functioning society, businesses, insurance companies, and members of the public depend on a reliable and competent jury system. In a civil case in which a defendant injures a plaintiff and a damage award must be determined, civil jurors must translate their subjective, qualitative sense about the severity of the plaintiff's injury into dollars. Yet many injuries are difficult to value, and civil juries receive limited guidance. Some jurors, too, have difficulty working with numbers. This research project examines how to improve jury damage award decision making so that damage awards are well-matched to the harm done. The project tests predictions from a new model of damage award decision making that focuses on the gist or underlying meaning that people derive from trial evidence. These predictions are (a) most jurors have a coherent sense of the gist of damage awards; (b) a gist-based understanding of numerical magnitude is essential for coherent judgments; and (c) this gist-based understanding can be facilitated with simple interventions to improve jury decision making. Three experiments are proposed to identify the jury damage award decision process and to test easily implemented and cost-effective solutions to reduce unwarranted inconsistency. The question of fair and consistent evaluation of injury arises in many business and insurance contexts, so effective solutions discovered through this research could prove useful in other domains beyond the courtroom. The legal system regularly requires fact finders to translate their qualitative impressions into quantitative judgments such as criminal sentences, fines, and damage awards. However, the relationship between qualitative and quantitative judgments in law is under-studied and under-theorized; no comprehensive theory yet explains it. The proposed research promises to move theory in the field forward by testing specific predictions derived from a new model of damage award decision making. The experiments extend fuzzy-trace theory to damage award decision making, testing a number of specific predictions about meaningfulness and relative gist of anchors (or benchmarks), relevant and irrelevant emotional evidence, and the utility of instructions about how to scale damages. Specifically, the first experiment investigates how potential jurors interpret the gist of case facts, how that influences damage awards, and whether providing meaningful anchor amounts reduces variability in award amounts. The second experiment focuses on effects of relevant and irrelevant emotion, and on the mapping process--how simple questions can help orient potential jurors to the magnitude of damages--with the aim of reducing variability in award judgments. The third experiment tests our new model in a realistic mock civil jury setting, including jury deliberations. Data collection will include multiple measures of numeracy (facility with numbers), allowing the researchers to determine how numeracy influences decision making about dollars in the context of civil damage awards.