It takes the average reader to read Frontiers in Climate Science by Dan Li (Professor of philosophy)
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When climate scientists face research questions that lack paradigmatic solutions, they seek methods and explanations elsewhere. This dissertation is dedicated to offering analyses and improvement of foreign methods in climate science. I do so by first explicating the inferential strategies behind these methods and then articulating how we can improve these methods to answer certain questions. After a brief introduction to philosophy of climate science and the naturalistic tradition of philosophy of science, in Ch. 2, I analyze a "missing tree ring" debate regarding whether trees could miss the growth of their annual ring at a large scale due to extreme environmental conditions. In Ch. 3, I discuss machine learning as automated induction and show that various problems of induction anticipated by philosophers are concrete obstacles in machine learning practice and theory. I then explain how to select variables that can help make the inductive leap to mitigate these problems with examples from climate science. In Ch. 4, I study how scientists can meaningfully apply network modeling on climate data. Broadly, this dissertation makes two contributions. First, it aims to help scientists. Each case study is accompanied with methodological suggestions that can be implemented as scientific research projects. Second, this dissertation demonstrates doing "philosophy in science," that is "raising a scientific problem, using philosophical tools to address it, and making a scientific proposal" (Pradeu et al. 2021). Thinking through these cases, I show that ideas should run before computation. Thus, this dissertation is also a showcase of how philosophy and science can benefit from each other by paying attention to the reasoning behind each.
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