Reconciling Bayesian Epistemology and Narration-based Approaches to Judiciary Fact-finding

Rafal Urbaniak
(Ghent University (Belgium) and University of Gdansk (Poland))

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.

In Jérôme Lang: Proceedings Sixteenth Conference on Theoretical Aspects of Rationality and Knowledge (TARK 2017), Liverpool, UK, 24-26 July 2017, Electronic Proceedings in Theoretical Computer Science 251, pp. 504–514.
Published: 25th July 2017.

ArXived at: http://dx.doi.org/10.4204/EPTCS.251.37 bibtex PDF
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