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

TL;DR

This paper explores Putnam's distinction between critical and explanatory tendencies using machine learning to reinterpret and analyze their philosophical significance and interdependence.

Contribution

It introduces a novel machine learning perspective to interpret and reconstruct Putnam's tendencies, linking philosophy of science with computational models.

Findings

Reconstructed Putnam's tendencies through machine learning interpretation

Established the biconditional necessity of critical and explanatory tendencies

Provided a new conceptual framework connecting philosophy and machine learning

Abstract

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.