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

TL;DR

This paper proposes a probabilistic framework that reconciles Bayesian epistemology with narrative-based approaches to judicial fact-finding, addressing their conceptual differences.

Contribution

It introduces a novel Bayesian model that formalizes the evaluation of competing narratives in legal fact-finding, bridging two influential perspectives.

Findings

Provides a formal probabilistic method for narrative evaluation

Shows how Bayesian principles can incorporate narrative dynamics

Bridges gap between classical legal probabilism and narrative approaches

Abstract

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.