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When are purely predictive models best?

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
Creators:
CreatorsEmailORCID
Northcott, Robertr.northcott@bbk.ac.uk
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
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: https://doi.org/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
Date: December 2017
Page Range: pp. 631-656
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/15383

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