Gradability across Science and Epistemic Practices – GRASP

Contrary to various terms in ordinary language, like tall or nice, scientific success predicates and notions do not seem to be graded: “more proven” or “more true” sound almost like contradictory expressions. This project plans to explore how, despite these appearances, gradability is central across science and its pathways toward results. By providing a gradualist picture of how success is reached at intermediary steps across practices, we aim to better grasp how knowledge is extended across local contexts and how scientists may entertain high epistemic ideals without making success an unreachable goal.
The project is organized around three main focus areas. First, we focus on issues related to epistemic processes, such as approximation methods or return rates in gradable activities. Second, we investigate gradability within research results, such as explanations or proofs, with respect to their different epistemic values. Third, we analyze gradability relative to epistemic states such as belief, knowledge, or understanding, both at the individual and collective levels. The final task is devoted to cross-analyses, transversal notions, and potentially general lessons concerning how scientific progress should be pictured. We’ll try to provide paradigmatic examples and thereby refine the too-simplistic conceptual space between algorithmic activities and apparently all-or-nothing activities like discovering or proving something.
A strength of this project is that it adopts a transversal stance and puts together complementary expertise and subfields, namely, the philosophy of math and computer science, as well as the philosophy of science and epistemology. Indeed, while philosophical questions about these subfields are distinct and answers can be expected to remain local, we hypothesize that, by focusing on the nuts and bolts of activities, a common ground of recurrent questions, puzzles, and notions will emerge and provide fruitful cross-analyses.