Disputatio 9 (47):631-656 (2017)

Authors
Robert Northcott
Birkbeck, University of London
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.
Keywords prediction  models  explanation  elections  weather
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DOI 10.1515/disp-2017-0021
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References found in this work BETA

Thinking About Mechanisms.Peter K. Machamer, Lindley Darden & Carl F. Craver - 2000 - Philosophy of Science 67 (1):1-25.

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What’s so Special About Empirical Adequacy?Sindhuja Bhakthavatsalam & Nancy Cartwright - 2017 - European Journal for Philosophy of Science 7 (3):445-465.
Prediction Versus Accommodation in Economics.Robert Northcott - 2019 - Journal of Economic Methodology 26 (1):59-69.

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