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Robert Northcott

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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Caterina Marchionni

. Ed. by M. Boumans. Elsevier: 135–52. Basso, Alessandra; Chiara Lisciandra; and Caterina Marchionni. 2017. Hypothetical models in social science. In Springer Handbook of Model-Based Science , ed. by Magnani and Bertolotti. Springer: 413–33. Bokulich, Alisa. 2014. How the tiger bush got its stripes: ‘how possibly’ vs. ‘how actually’ model explanations. The Monist 97(3): 321–38. Bokulich, Alisa. 2009. Explanatory fictions. In Fictions in Science: Philosophical Essays on Modeling and Idealization , ed. by M. Suárez. London: Routledge, 91

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Veli-Pekka Parkkinen

References Anitschkow, Nikolai. 1913. Über die Veränderungen der Kanichnenaorta bei experimenteller Cholesterinsteatose. Beitrage zur pathologischen Anatomie und zur allgemeinen Pathologi 56: 379–404. Anitschkow, Nikolai. 1933. Experimental arteriosclerosis in animals. In Arteriosclerosis , ed. by Edmund. V. Cowdry. New York: The MacMillan Company, 271–322. Ankeny, Rachel. 2009. Model organisms as fictions. In Fictions in Science. Philosophical Essays on Modelling and Idealisation , ed. by Mauricio Suárez. London: Routledge, 194

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Maria Serban

. Dimensions of integration in interdisciplinary explanations of the origin of evolutionary novelty. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 44(4): 537–50. Love, Alan C.; and Nathan, Marco J. 2015. The idealization of causation in mechanistic explanation. Philosophy of Science 82(5): 761–74. Madzvamuse, Anotida; Gaffney, Eamonn A.; and Maini, Philip K. 2010. Stability analysis of non-autonomous reaction-diffusion systems: the effects of growing domains. Journal of

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Dana Matthiessen

.biomedcentral.com/1471-2105/8/S6/S5 > Matthiessen, Dana. 2015. Mechanistic explanation in systems biology: cellular networks. British Journal for the Philosophy of Science . < http://bjps.oxfordjournals.org/content/early/2015/04/09/bjps.axv011.full.pdf+html > McMullin, Ernan. 1985. Galilean idealization. Studies in the History and Philosophy of Science 16: 247–73. Mitchell, Sandra. 2008. Exporting causal knowledge in evolutionary and developmental biology. Philosophy of Science 75: 697–706. Morgan, Mary; and Morrison, Margaret. 1999. Models as Mediators