The application of Dempster-Shafer theory demonstrated with justification provided by legal evidence

Author:

Curley Shawn P.

Abstract

AbstractIn forecasting and decision making, people can and often do represent a degree of belief in some proposition. At least two separate constructs capture such degrees of belief: likelihoods capturing evidential balance and support capturing evidential weight. This paper explores the weight or justification that evidence affords propositions, with subjects communicating using a belief function in hypothetical legal situations, where justification is a relevant goal. Subjects evaluated the impact of sets of 1–3 pieces of evidence, varying in complexity, within a hypothetical legal situation. The study demonstrates the potential usefulness of this evidential weight measure as an alternative or complement to the more-studied probability measure. Subjects’ responses indicated that weight and likelihood were distinguished; that subjects’ evidential weight tended toward single elements in a targeted fashion; and, that there were identifiable individual differences in reactions to conflicting evidence. Specifically, most subjects reacted to conflicting evidence that supported disjoint sets of suspects with continued support in the implicated sets, although an identifiable minority reacted by pulling back their support, expressing indecisiveness. Such individuals would likely require a greater amount of evidence than the others to counteract this tendency in support. Thus, the study identifies the value of understanding evidential weight as distinct from likelihood, informs our understanding of the psychology of individuals’ judgments of evidential weight, and furthers the application and meaningfulness of belief functions as a communication language.

Publisher

Cambridge University Press (CUP)

Reference44 articles.

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Applications of Different Methods to Handle Uncertainty in Artificial Intelligence;Handling Uncertainty in Artificial Intelligence;2023

2. COMPUTER-BASED KNOWLEDGE MANAGEMENT FOR FUTURES LITERACY;12th International Scientific Conference “Business and Management 2022”;2022-05-24

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