When are Purely Predictive Models Best?

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 s...

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Publicado en:Disputatio: International Journal of Philosophy Vol. 9; no. 47; pp. 631 - 657
Autor principal: Northcott, Robert
Formato: Artículo
Publicado: Paradigm Publishing Services Dec2017
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Acceso en línea:Ver este registro en EBSCOhost
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        au: Northcott, Robert
        affil: Birkbeck College, University of London
      su:
        Prediction models
        Social sciences
        Explanation
        Elections
        Weather forecasting
      sug:
        subj:
          Prediction models
          Social sciences
          Explanation
          Elections
          Weather forecasting
      keyword:
        causation
        explanation
        idealization
        Prediction
        weather
      ab: 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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    language: English
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