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.
