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Learning from Non-Causal Models

Nappo, Francesco (2020) Learning from Non-Causal Models. [Preprint]

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Abstract

This paper defends the thesis of learning from non-causal models: viz. that the study of some model can prompt justified changes in one’s confidence in empirical hypotheses about a real-world target in the absence of any known or predicted similarity between model and target with regards to their causal features. Recognizing that we can learn from non-causal models matters not only to our understanding of past scientific achievements, but also to contemporary debates in the philosophy of science. At one end of the philosophical spectrum, my thesis undermines the views of those who, like Cartwright (2009), follow Hesse (1963) in restricting the possibility of learning from models to only those situations where a model identifies some causal factors present in the target. At the other end of the spectrum, my thesis also helps undermine some extremely permissive positions, e.g., Grüne-Yanoff’s (2009, 2013) claim that learning from a model is possible even in the absence of any similarity at all between model and target. The thesis that we can learn from non-causal models offers a cautious middle ground between these two extremes.


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Item Type: Preprint
Creators:
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Nappo, Francesco
Additional Information: Published in Erkenntnis, forthcoming.
Keywords: Scientific Models, Mary Hesse, non-causal explanations, learning from models
Subjects: General Issues > Models and Idealization
Depositing User: Dr. Francesco Nappo
Date Deposited: 05 Oct 2021 04:47
Last Modified: 05 Oct 2021 04:48
Item ID: 19642
Official URL: https://link.springer.com/article/10.1007/s10670-0...
Subjects: General Issues > Models and Idealization
Date: 2020
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/19642

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