Permissible idealizations for the purpose of prediction |
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Authors: | Michael Strevens |
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Affiliation: | 1. Department of Philosophy, University of British Columbia, 1866 Main Mall, Buchanan E370, Vancouver, BC, Canada, V6T 1Z1;2. Department of Philosophy, Mount Allison University, 63D York St, Sackville, New Brunswick, E4L 1G9, Canada;3. School of Sustainability and School of Historical, Philosophical and Religious Studies, Arizona State University, Wrigley Hall, 800 Cady Mall #108, Tempe, AZ, 85281, USA;1. Macquarie University, Department of Philosophy, North Ryde, NSW, 2109, Australia;2. The University of Sydney, Department of Philosophy & Charles Perkins Centre, Sydney, NSW, 2006, Australia |
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Abstract: | Every model leaves out or distorts some factors that are causally connected to its target phenomenon—the phenomenon that it seeks to predict or explain. If we want to make predictions, and we want to base decisions on those predictions, what is it safe to omit or to simplify, and what ought a causal model to describe fully and correctly? A schematic answer: the factors that matter are those that make a difference to the target phenomenon. There are several ways to understand differencemaking. This paper advances a view as to which is the most relevant to the forecaster and the decision-maker. It turns out that the right notion of differencemaking for thinking about idealization in prediction is also the right notion for thinking about idealization in explanation; this suggests a carefully circumscribed version of Hempel’s famous thesis that there is a symmetry between explanation and prediction. |
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Keywords: | Prediction Idealization Modeling Difference-making Causal relevance |
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