Addressing Argumentation Puzzles with Model-based Diagnosis

In law, and, consequently, in AI & Law, argumentation, scenario-modeling, and the combination of both, are the traditional ways of theorizing about judicial reasoning and legal truth, while probabilistic reasoning has traditionally been treated with suspicion2. Nevertheless, because of the growing relevance of forensic scientific evidence, a proper integration of probabilistic reasoning into the argumentation process is increasingly a debated problem. Pollock presents in [1] a lucid philosophical critique on how probabilistic methods approach the problem of justification, in the form of some interesting legal puzzles. He gives the following case: Jones says that the gunman had a moustache. Paul says that Jones was looking the other way and did not see what happened. Jacob says that Jones was watching carefully and had a clear view of the gunman. This is an example of “collective defeat” (Paul vs Jacob), which results in a “zombie argument” (Jones’). From this story, Pollock targets some intuitive properties. (1) Given the conflict of witnesses, we should not believe to Jones’ claim carelessly. (2) If we consider Paul more trustworthy than Jacob, Paul’s claim should be justified, but to a lesser degree. (3) Conversely, if Jacob had confirmed Paul’s claim, its “degree of justification” should have increased.3 Pollock gives then a preliminary, elaborated proposal for degrees of justification, based on “probable probabilities”. Working with a different – in one sense opposite – perspective, we have found an alternative solution to his quest.