This paper tries to express a critical point of view on the computational turn in philosophy by looking at a specific field of study: philosophy of science. The paper starts by briefly discussing the main contributions that information and communication technologies have given to the rising of computational philosophy of science, and in particular to the cognitive modelling approach. The main question then arises, concerning how computational models can cope with the presence of tacit knowledge in science. Would it be (...) possible to develop new ways of handling this specific type of knowledge, in order to incorporate it in computational models of scientific thinking? Or should tacit knowledge lead us to other approaches in using computer sciences to model scientific cognition? These questions are addressed by making reference to a detailed case study of a recent innovation development in the field of biotechnology. (shrink)

The concept of tacit knowledge is widely used in social sciences to refer to all those knowledge that cannot be codified and have to be transferred by personal contacts. All this literature has been affected by two kind of biases : (1) the interest has been focused more on the result (tacit knowledge) than on the process (implicit learning); (2) tacit knowledge has been somehow reduced to physical skills or know-how; other possible forms of tacit knowledge have been neglected. These (...) two biases seem interconnected one with each other. A greater consideration of the role and relevance of implicit learning allows us to consider tacit knowledge as something more than pure physical skills or know how. This is the first step in order to develop more detailed categorisation of the different forms that tacit knowledge can assume. (shrink)