Putnam's Critical and Explanatory Tendencies Interpreted from a Machine Learning Perspective

Introduction Making sense of theory choice in normal and across extraordinary science is central to philosophy of science. The emergence of machine learning models has the potential to act as a wrench in the gears of current debates. In this paper, I will attempt to reconstruct the main movements that lead to and came out of Putnam's critical and explanatory tendency distinction, argue for the biconditional necessity of the tendencies, and conceptualize that wrench through a machine learning interpretation of my claim. Some preliminary definitions and statements of assumptions are in order. Kuhn's picture of normal versus extraordinary science is presented in his 1962 book "The Structure of Scientific Revolution". In a short caricature of the distinction, normal science takes place within paradigms and extraordinary science takes place across paradigms.