The purpose of this essay is to present and analyse the basic assumptions of Leszek Nowak’s conception of the unity of science. According to Nowak, the unity of science is manifested in the common application of the method of idealisation in scientific research. In accordance with his conception, regardless of the discipline they represent, researchers go through the same stages in building a theory. Two key ones among them are: introducing idealising assumptions into the representation and then their concretisation. In (...) this view, idealisation is the basis of the scientific method, while other cognitive procedures complement it. Nowak’s conception has particular relevance in the context of the dispute between naturalism and anti-naturalism and in the context of the continuing rift between social scientists and natural scientists. It calls into question the anti-naturalist thesis of the ontological uniqueness of social sciences and the resulting methodological consequences. I argue that Nowak’s conception is a cognitively valuable contribution to the contemporary epistemology of science, but it also has weaknesses, mainly due to the limitations of applying the idealisation-concretisation scheme in research practice. For it turns out, as I point out in this essay, that many idealising assumptions are not subject to concretisation and that concretisations do not always condition an increase in the explanatory and/or predictive power of the representations. (shrink)

We review publications in automated scientific discovery using deep learning, with the aim of shedding light on problems with strong connections to philosophy of science, of physics in particular. We show that core issues of philosophy of science, related, notably, to the nature of scientific theories; the nature of unification; and of causation loom large in scientific deep learning. Therefore, advances in deep learning could, and ideally should, have impact on philosophy of science, and vice versa. We suggest lines of (...) further research, and highlight the role ‘theory-based’ AI could have in future developments of the field. (shrink)

The debate between the defenders of explanatory unification and explanatory pluralism has been ongoing from the beginning of cognitive science and is one of the central themes of its philosophy. Does cognitive science need a grand unifying theory? Should explanatory pluralism be embraced instead? Or maybe local integrative efforts are needed? What are the advantages of explanatory unification as compared to the benefits of explanatory pluralism? These questions, among others, are addressed in this Synthese’s special issue. In the introductory paper, (...) we discuss the background of the questions, distinguishing integrative theorizing from building unified theories. On the one hand, integrative efforts involve collaboration between various disciplines, fields, approaches, or theories. These efforts could even be quite temporary, without establishing any long-term institutionalized fields or disciplines, but could also contribute to developing new interfield theories. On the other hand, unification can rely on developing complete theories of mechanisms and representations underlying all cognition, as Newell’s “unified theories of cognition”, or may appeal to grand principles, as predictive coding. Here, we also show that unification in contemporary cognitive science goes beyond reductive unity, and may involve various forms of joint efforts and division of explanatory labor. This conclusion is one of the themes present in the content of contributions constituting the special issue. (shrink)

Different why-questions emerge under different contexts and require different information in order to be addressed. Hence a relevance relation can hardly be invariant across contexts. However, what is indeed common under any possible context is that all explananda require scientific information in order to be explained. So no scientific information is in principle explanatorily irrelevant, it only becomes so under certain contexts. In view of this, scientific thought experiments can offer explanations, should we analyze their representational strategies. Their representations involve (...) empirical as well as hypothetical statements. I call this the “representational mingling” which bears scientific information that can explain events. Buchanan’s thought experiment from constitutional economics is examined to show how mingled representations explain. (shrink)

This paper considers the importance of unification in the context of developing scientific theories. I argue that unifying hypotheses are not valuable simply because they are supported by multiple lines of evidence. Instead, they can be valuable because they guide experimental research in different domains in such a way that the results from those experiments inform the scope of the theory being developed. I support this characterization by appealing to the early development of quantum theory. I then draw some comparisons (...) with discussions of robustness reasoning. (shrink)

This paper considers the importance of unification in the context of developing scientific theories. I argue that unifying hypotheses are not valuable simply because they are supported by multiple lines of evidence. Instead, they can be valuable because they guide experimental research in different domains in such a way that the results from those experiments inform the scope of the theory being developed. I support this characterization by appealing to the early development of quantum theory. I then draw some comparisons (...) with discussions of robustness reasoning. (shrink)