The similarity approach stands as a significant attempt to defend scientific realism from the attack of the pessimistic meta-induction. The strategy behind the similarity approach is to shift from an absolute notion of truth to the more flexible one of truthlikeness. Nonetheless, some authors are not satisfied with this attempt to defend realism and find that the notion of truthlikeness is not fully convincing. The aim of this paper is to analyze and understand the reasons of this dissatisfaction. Our thesis (...) is that the dissatisfaction with the notion of truthlikeness concerns the double role that this notion plays within the similarity approach: This notion plays both a regulative role in the conception of theories and a constitutive one in their selection. (shrink)

In artificial intelligence (AI), a number of criticisms were raised against the use of probability for dealing with uncertainty. All these criticisms, except what in this article we call the non-adequacy claim, have been eventually confuted. The non-adequacy claim is an exception because, unlike the other criticisms, it is exquisitely philosophical and, possibly for this reason, it was not discussed in the technical literature. A lack of clarity and understanding of this claim had a major impact on AI. Indeed, mostly (...) leaning on this claim, some scientists developed an alternative research direction and, as a result, the AI community split in two schools: a probabilistic and an alternative one. In this article, we argue that the non-adequacy claim has a strongly metaphysical character and, as such, should not be accepted as a conclusive argument against the adequacy of probability. (shrink)