用机器学习视角重释普特南的批判与解释倾向之分
Putnam's Critical and Explanatory Tendencies Interpreted from a Machine Learning Perspective
- 将普特南的理论选择思想转化为机器学习中的认知机制
- 论证批判与解释倾向互为必要条件，缺一不可
- 适合对科学哲学与人工智能交叉研究感兴趣的读者
科学哲学中理论选择问题——无论是常规科学还是范式转变中的选择——始终是核心议题。机器学习模型的兴起为当前争论带来了新的挑战。本文试图重构普特南关于批判性倾向与解释性倾向之间关系的主要发展脉络，论证二者之间的双向必要性，并通过机器学习视角来概念化这一挑战，将理论选择过程建模为一种可计算的认知动态。
原文摘要 · Abstract (English)
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.
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