Erkenntnis:1-21 (forthcoming)

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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, follow Hesse 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 :81–99, 2009, Philos Sci 80: 850–861, 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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DOI 10.1007/s10670-020-00310-8
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References found in this work BETA

Fact, Fiction, and Forecast.Nelson Goodman - 1955 - Harvard University Press.
Nature's Capacities and Their Measurement.Nancy Cartwright - 1989 - Oxford, England: Oxford University Press.
Models and Analogies in Science.Mary Hesse - 1965 - British Journal for the Philosophy of Science 16 (62):161-163.
Fact, Fiction, and Forecast.Nelson Goodman - 1955 - Philosophy 31 (118):268-269.

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Citations of this work BETA

Close encounters with scientific analogies of the third kind.Francesco Nappo - 2021 - European Journal for Philosophy of Science 11 (3):1-20.
Confirmation by Analogy.Francesco Nappo - 2022 - Synthese 200 (1):1-26.

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