Consistency and credibility in legal reasoning: A Bayesian network approach

Witness credibility is important for establishing testimonial value. The story model posits that people construct narratives from evidence but does not explain how credibility is assessed. Formal approaches use Bayesian networks (BN) to represent legal evidence. Recent empirical work suggests people might also reason using qualitative causal networks. In two studies, participants read a realistic trial transcript and judge guilt and witness credibility. Study 1 varied testimonial consistency and defendant character. Guilt and credibility assessments were affected by consistency but not prior convictions. Study 2 constructed a BN to represent consistency issues. Individual parameter estimates were elicited for the corresponding BN to compute posterior predictions for guilt and credibility. The BN provided a good model for overall and individual guilt and credibility ratings. These results suggest people construct causal models of the evidence and consider witness credibility. The BN approach is a promising direction for future research in legal reasoning.