An Aggregation Theory of Character Evidence
本文提出聚合证据理论，运用统计估计与数据聚合原理，分析品格证据对判决准确性的影响，并解释法院为何偏离禁止品格证据的规则。
Courts frequently depart from Federal Rule of Evidence 404, which prohibits evidence of a person’s prior acts to prove that the person acted according to a certain character. This leads to verdicts that are unpredictable and based on behavior not at issue in a case. I develop a theory of aggregation evidence, a new concept that draws on principles of estimation and data aggregation in statistics and ties together evidence from a broad range of contexts. I apply this theory to analyze the effects of character evidence on accuracy and to understand why and when courts depart from the rule against character evidence. I show that a type of character evidence that I call objective-chance evidence stands apart from other forms of character evidence in its ability to improve accuracy. I then argue that a formal exception for this type of evidence may lead to a more coherent rule.