La Comprensión Científica en Ausencia de Explicación

[eng] This thesis challenges a central tenet in the philosophy of science: that explanation is necessary for scientific understanding. While explanatory accounts of understanding dominate philosophical discourse, they seem ill-suited for analyzing scientific cases where explanation is absent. This thesis argues that genuine scientific understanding of a phenomenon is possible, even when scientists lack a comprehensive explanation of it. The thesis explores this claim through three detailed case studies. The first examines Ludwig Prandtl's intuitive grasp of flow separation, achieved prior to his formalization of the boundary layer theory. The second analyzes how the IHME COVID-19 model provided valuable predictions about the pandemic's trajectory that guided actions and informed decisions. The third investigates how AlphaFold, a deep neural network, achieves remarkable success in predicting protein structures without simulating the physical processes of protein folding. Each case study highlights a distinct aspect of scientific understanding without explanation. Prandtl's work demonstrates the role of intuitive grasp and visualization in achieving understanding prior to formulating an explanation. The IHME model exemplifies how understanding can arise from purely descriptive regularity patterns, even in the absence of causal representation. AlphaFold reveals how predictive accuracy, grounded in empirically based assumptions, can constitute a form of understanding even when causal mechanisms remain opaque. The thesis concludes that scientific understanding without explanation is not only possible but also constitutes a significant epistemic achievement. Recognizing this form of understanding requires a re-evaluation of our traditional epistemic standards and expands our view of the aims of scientific inquiry and the diverse ways in which scientists understand the world.