Northcott, Robert (2017) When are purely predictive models best? Disputatio. pp. 631-656.
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Abstract
Can purely predictive models be useful in investigating causal systems? I argue “yes”. Moreover, in many cases not only are they useful, they are essential. The alternative is to stick to models or mechanisms drawn from well-understood theory. But a necessary condition for explanation is empirical success, and in many cases in social and field sciences such success can only be achieved by purely predictive models, not by ones drawn from theory. Alas, the attempt to use theory to achieve explanation or insight without empirical success therefore fails, leaving us with the worst of both worlds—neither prediction nor explanation. Best go with empirical success by any means necessary. I support these methodological claims via case studies of two impressive feats of predictive modelling: opinion polling of political elections, and weather forecasting.
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Item Type: | Published Article or Volume | ||||||
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Keywords: | Prediction, explanation, weather, causation, idealization | ||||||
Subjects: | Specific Sciences > Complex Systems General Issues > Explanation General Issues > Models and Idealization General Issues > Operationalism/Instrumentalism Specific Sciences > Sociology |
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Depositing User: | Dr Robert Northcott | ||||||
Date Deposited: | 27 Nov 2018 16:45 | ||||||
Last Modified: | 27 Nov 2018 16:45 | ||||||
Item ID: | 15383 | ||||||
Journal or Publication Title: | Disputatio | ||||||
Official URL: | https://www.degruyter.com/downloadpdf/j/disp.2017.... | ||||||
DOI or Unique Handle: | 10.1515/disp-2017-0021 | ||||||
Subjects: | Specific Sciences > Complex Systems General Issues > Explanation General Issues > Models and Idealization General Issues > Operationalism/Instrumentalism Specific Sciences > Sociology |
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Date: | December 2017 | ||||||
Page Range: | pp. 631-656 | ||||||
URI: | https://philsci-archive.pitt.edu/id/eprint/15383 |
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