法律确证的概率模型

IF 0.7 2区 社会学 Q2 LAW International Journal of Evidence & Proof Pub Date : 2020-01-01 DOI:10.1177/1365712719864608
R. Urbaniak, Pavel Janda
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引用次数: 0

摘要

其目的是开发一个合理的法律确证的概率模型,以回应科恩对法律推理的概率方法的攻击。科恩的一个论点是,没有证据支持的概率度量,能够令人满意地捕捉到独立证人证明同一命题的真实性(或独立证据集中于某一主张)的情况——这种现象被称为确证(或趋同)。我们研究了科恩讨论的几种概率测度的性质,讨论了科恩对这些测度的批评,并发展了我们自己的概率测度。最后,我们提供了一种概率的确证措施,它避开了迄今为止讨论过的临界点。
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Probabilistic models of legal corroboration
The aim is to develop a sensible probabilistic model of legal corroboration in response to an attack on the probabilistic approach to legal reasoning due to Cohen. One of Cohen’s arguments is that there is no probabilistic measure of evidential support which satisfactorily captures the situation in which independent witnesses testify to the truth of the same proposition (or independent pieces of evidence converge on a certain claim)—the phenomenon called corroboration (or convergence). We investigate the properties of several probabilistic measures discussed by Cohen, discuss Cohen’s criticism of those measures, and develop our own. Finally, we offer a probabilistic measure of corroboration that evades the critical points raised against the ones discussed so far.
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来源期刊
CiteScore
2.30
自引率
20.00%
发文量
15
期刊最新文献
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