Project start: 1.10.2025.

The project explores key aspects of explanation in the biomedical sciences, focusing on two main questions: (1) How do we explain complex biological systems by selectively highlighting important features while ignoring many details? (2) What makes classifications explanatory, and why are some classificatory systems explanatorily more successful than others?

Objective 1: In the biomedical sciences, models are crucial for prediction and explanation. They selectively capture significant aspects of biological systems while omitting others. Simple but predictively successful models are often used to explain complex living systems—for example, the model of gene regulation in prokaryotes. This raises the question of what enables such success. The project engages with recent philosophical debates on causal explanation that emphasize the importance of invoking diverse causal structures—such as pathways and cascades—rather than relying solely on mechanisms. Case studies will include explanatory models from cell biology, molecular biology and medicine. The ultimate aim is to develop, refine, and defend a philosophical theory of causal explanation suited to the specificities of biomedical science.

Objective 2: The project will analyze what makes certain scientific categories (referred to in philosophical literature as natural kinds) explanatory. For instance, what, according to some authors, makes the RDoC system of psychiatric classification more explanatorily powerful than the DSM-5 approach? Traditional philosophical accounts suggest that categories are explanatory when they can be linked to privileged causal properties. However, empirical practice in the biomedical sciences reveals a more complex picture: such privileged properties are often difficult to identify, and instead, a range of causal factors may jointly contribute to the clustering of certain traits. To address how classification should be approached in these cases, which causal factors should be emphasized, and what makes such classifications explanatory, the project will examine debates on classification in medical nosology, as well as in contemporary genomics and proteomics. The ultimate aim is to identify criteria for choosing classificatory systems based on their explanatory value.