A defense of Columbo (and of the use of Bayesian inference in forensics): A multilevel introduction to probabilistic reasoning

TL;DR

This paper defends the use of Bayesian inference in forensic reasoning, clarifies misconceptions, and emphasizes the importance of proper probabilistic reasoning, including priors and evidence evaluation, with practical examples and Bayesian network introduction.

Contribution

It provides a clear, accessible introduction to probabilistic reasoning in forensics, addressing misconceptions and illustrating the correct use of Bayesian inference with practical examples.

Findings

No probabilistic pitfall in Columbo's reasoning

Importance of all available information in belief updating

Bayesian networks aid formal probabilistic reasoning

Abstract

Triggered by a recent interesting New Scientist article on the too frequent incorrect use of probabilistic evidence in courts, I introduce the basic concepts of probabilistic inference with a toy model, and discuss several important issues that need to be understood in order to extend the basic reasoning to real life cases. In particular, I emphasize the often neglected point that degrees of beliefs are updated not by `bare facts' alone, but by all available information pertaining to them, including how they have been acquired. In this light I show that, contrary to what claimed in that article, there was no "probabilistic pitfall" in the Columbo's episode pointed as example of "bad mathematics" yielding "rough justice". Instead, such a criticism could have a `negative reaction' to the article itself and to the use of Bayesian reasoning in courts, as well as in all other places in which probabilities need to be assessed and decisions need to be made. Anyway, besides introductory/recreational aspects, the paper touches important questions, like: role and evaluation of priors; subjective evaluation of Bayes factors; role and limits of intuition; `weights of evidence' and `intensities of beliefs' (following Peirce) and `judgments leaning' (here introduced), including their uncertainties and combinations; role of relative frequencies to assess and express beliefs; pitfalls due to `standard' statistical education; weight of evidences mediated by testimonies. A small introduction to Bayesian networks, based on the same toy model (complicated by the possibility of incorrect testimonies) and implemented using Hugin software, is also provided, to stress the importance of formal, computer aided probabilistic reasoning.