In this article we defend the inferential view of scientific models and idealisation. Models are seen as ''inferential prostheses'' (instruments for surrogative reasoning) construed by means of an idealisation-concretisation process, which we essentially understand as a kind of counterfactual deformation procedure (also analysed in inferential terms). The value of scientific representation is understood in terms not only of the success of the inferential outcomes arrived at with its help, but also of the heuristic power of representation and their capacity to correct and improve our models. This provides us with an argument against Sugden's account of credible models: the likelihood or realisticness (their ''credibility'') is not always a good measure of their acceptability. As opposed to ''credibility'' we propose the notion of ''enlightening'', which is the capacity of giving us understanding in the sense of an inferential ability.

The surprise exam paradox has attracted the attention of prominent logicians, mathematicians and philosophers for decades. Although the paradox itself has been resolved at least since Quine (1953), some aspects of it are still being discussed. In this paper we propose, following Sober (1998), to translate the paradox into the language of game theory to clarify these aspects. Our main conclusions are that a much simpler game theoretic analysis of the paradox is possible, which solves most of the puzzles related to it, and that this way of analysing the paradox can also throw light on our comprehension of the pragmatics of linguistic communication.

Scientific research is reconstructed as a language game along the lines of Robert Brandom's inferentialism. Researchers are assumed to aim at persuading their colleagues of the validity of some claims. The assertions each scientist is allowed or committed to make depend on her previous claims and on the inferential norms of her research community. A classification of the most relevant types of inferential rules governing such a game is offered, and some ways in which this inferentialist approach can be used for assessing scientific knowledge and practices are explored. Some similarities and differences with a game-theoretic analysis are discussed.

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