The diverse aims of science

Studies in History and Philosophy of Science Part A 53:71-80 (2015)
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Abstract

There is increasing attention to the centrality of idealization in science. One common view is that models and other idealized representations are important to science, but that they fall short in one or more ways. On this view, there must be an intermediary step between idealized representation and the traditional aims of science, including truth, explanation, and prediction. Here I develop an alternative interpretation of the relationship between idealized representation and the aims of science. In my view, continuing, widespread idealization calls into question the idea that science aims for truth. I argue that understanding must replace truth as the ultimate epistemic aim of science. Additionally, science has a wide variety aims, epistemic and non-epistemic, and these aims motivate different kinds of scientific products. Finally, I show how these diverse aims---all rather distant from truth---result in the expanded influence of social values on science.

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Angela Potochnik
University of Cincinnati

Citations of this work

Understanding from Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.
Scientific progress: Knowledge versus understanding.Finnur Dellsén - 2016 - Studies in History and Philosophy of Science Part A 56 (C):72-83.
Beyond Explanation: Understanding as Dependency Modeling.Finnur Dellsén - 2018 - British Journal for the Philosophy of Science (4):1261-1286.
Explicating objectual understanding: taking degrees seriously.Christoph Baumberger - 2019 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 1:1-22.

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References found in this work

Depth: An Account of Scientific Explanation.Michael Strevens - 2008 - Cambridge, Mass.: Harvard University Press.
The Dappled World: A Study of the Boundaries of Science.Nancy Cartwright - 1999 - New York, NY: Cambridge University Press.
True Enough.Catherine Z. Elgin - 2017 - Cambridge: MIT Press.

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