Quantitative Judgments in Law: Studies of Damage Award Decision Making

为了拥有一个运转良好的社会，企业、保险公司和公众都依赖于一个可靠和称职的陪审团制度。在被告伤害原告并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、定性的感觉转化为美元。然而，许多伤害很难评估，民事陪审团得到的指导也有限。一些陪审员在处理数字方面也遇到了困难。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，该模型关注的是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主旨有连贯的认识；(B)对数字大小的依据理解对于连贯的判决至关重要；以及(C)通过简单的干预措施改善陪审团的决策，可以促进这种基于主旨的理解。提出了三个实验，以确定陪审团损害赔偿裁决的决定过程，并测试易于实施和成本效益高的解决方案，以减少不必要的不一致。公平和一致的伤害评估问题出现在许多商业和保险背景下，因此通过这项研究发现的有效解决方案可能被证明在法庭以外的其他领域有用。法律制度经常要求事实调查员将他们的定性印象转化为量化判决，如刑事判决、罚款和损害赔偿。然而，法律中定性判决和定量判决之间的关系还没有得到充分的研究和理论化，还没有全面的理论来解释这一关系。这项拟议的研究有望通过检验从损害赔偿决策的新模型中得出的具体预测来推动该领域的理论向前发展。这些实验将模糊跟踪理论扩展到损害赔偿决策中，测试了一些关于锚(或基准)的意义和相对要点、相关和无关情感证据的具体预测，以及关于如何衡量损害赔偿的指示的实用性。具体地说，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变异性。第二个实验侧重于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的大小--目的是减少裁决判决的可变性。第三个实验是在现实的模拟民事陪审团环境中测试我们的新模型，包括陪审团审议。数据收集将包括多种计算能力(便于使用数字)，使研究人员能够确定在民事损害赔偿的背景下，计算能力如何影响对美元的决策。

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.