Reconciling Bayesian Epistemology and Narration-based Approaches to Judiciary Fact-finding
This work addresses a theoretical problem in legal philosophy and evidence theory, offering a novel integration of competing methodologies.
The paper tackles the challenge of reconciling Bayesian epistemology with narrative-based approaches in judicial fact-finding by developing a probabilistic framework that formalizes narrative evaluation criteria.
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.