调和贝叶斯认识论与司法事实认定的叙事方法

R. Urbaniak
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引用次数: 0

摘要

法律概率论(LP)声称,在司法事实调查中,信念程度的建模方式与标准贝叶斯认识论中信念程度的建模方式完全相同。经典法律概率论(CLP)补充说,如果证据表明有罪的可信度高于适当的有罪概率阈值,则定罪是正当的。这些观点在很多方面受到质疑,特别是来自所谓的叙事方法的支持者,根据这种方法,事实发现者的决定是对所发生事件的相互竞争的叙述之间动态相互作用的结果。我发展出一种贝叶斯认识论的方法来理解叙事方法。为此,我制定了一个概率框架,根据叙事方法中使用的非正式评估标准的正式解释,来评估相互竞争的叙述。
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Reconciling Bayesian Epistemology and Narration-based Approaches to Judiciary Fact-finding
Legal probabilism (LP) claims the degrees of conviction in juridical fact-finding are to be modeled exactly the way degrees of beliefs are modeled in standard bayesian epistemology. Classical legal probabilism (CLP) adds that the conviction is justified if the credence in guilt given the evidence is above an appropriate guilt probability threshold. The views are challenged on various counts, especially by the proponents of the so-called narrative approach, on which the fact-finders' decision is the result of a dynamic interplay between competing narratives of what happened. I develop a way a bayesian epistemologist can make sense of the narrative approach. I do so by formulating a probabilistic framework for evaluating competing narrations in terms of formal explications of the informal evaluation criteria used in the narrative approach.
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