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Ilkka Niiniluoto

), From Instrumentalism to Constructive Realism , Kluwer, Dordrecht. Lange, Marc (2015), “On ‘Minimal Model Explanations’: A Reply to Batterman and Rice”, Philosophy of Science 82, 292–305. Liu, Chang (1999), “Approximation, Idealization, and Laws of Nature”, Synthese 118, 229–256. Mäki, Uskali (1994), “Isolation, Idealization and Truth in Economics”, in Bert Hamminga and Neil De Marchi (eds.), Idealization in Economics , Rodopi, Amsterdam, 147–168. Mäki, Uskali (2009), “MISSing the World: Models as Isolations and Credible Surrogate Systems

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Marcin Miłkowski

., (Ed.) (2009). Fictions in science: Philosophical essays on modeling and idealization. Vol. 4. New York: Routledge. Sun, R. (2009). Theoretical status of computational cognitive modeling. Cognitive Systems Research, 10(2), 124-140. Tolman, E. C. (1939). Prediction of vicarious trial and error by means of the schematic sowbug. Psychological Review, 46(4), 318-336. Webb, B. (2009). Animals versus animats: Or why not model the real iguana? Adaptive Behavior, 17(4) (July 28), 269-286. Weber, B. H., & Depew

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Friedel Weinert

Abstract

This paper critically analyzes the fiction-view of scientific modeling, which exploits presumed analogies between literary fiction and model building in science. The basic idea is that in both fiction and scientific modeling fictional worlds are created. The paper argues that the fiction-view comes closest to certain scientific thought experiments, especially those involving demons in science and to literary movements like naturalism. But the paper concludes that the dissimilarities prevail over the similarities. The fiction-view fails to do justice to the plurality of model types used in science; it fails to realize that a function like idealization only makes sense in science because models, unlike works of fiction, can be de-idealized; it fails to distinguish sufficiently between the make-believe (fictional) worlds created in fiction and the hypothetical (as-if) worlds envisaged in models. Representation characterized in the fiction-view as a license to draw inferences does not sufficiently distinguish between inferences in fiction from inferences in scientific modeling. To highlight the contrast the paper proposes to explicate representation in terms of satisfaction of constraints.

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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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Filip Kobiela

Abstract

The paper addresses the family of questions that arose from the field of interactions between phenomenology and the cognitive sciences. On the one hand, apparently partial coextensivity of research domain of phenomenology and the cognitive sciences sets the goal of their cooperation and mutual inspiration. On the other hand, there are some obstacles on the path to achieve this goal: phenomenology and the cognitive sciences have different traditions, they speak different languages, they have adopted different methodological approaches, and last but not least, their prominent exponents exhibits different styles of thinking. In order to clarify this complicated area of tensions, the paper presents the results of philosophical reflections of such topics as: 1) philosophical presuppositions and postulates of the cognitive sciences 2) abstraction of some phenomena during idealisation and the dialectical model of science's development 3) argumentation based on prediction of future development of the cognitive sciences. This finally leads to the formulation of a phenomenology-based postulate for adequate model of mind and the discussion of humanistic dimension of cognitive sciences.

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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

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Matthieu Gallais

References Currie, G., 1990, The Nature of Fiction , Cambridge University Press. Frigg, R., 2010a, “Models and Fiction”, Synthese 172(2), 251–268. Frigg, R., 2010b, “Fiction in Science”, in Woods, J. (ed.): Fictions and Models: New Essays , Munich, Philosophia Verlag, 247–287. Held, C., 2009, “When Does a Scientific Theory Describe Reality?” in Suárez, M. (ed.): Fictions in Science: Philosophical Essays on Modeling and Idealization , Taylor & Francis, 139-157. Hintikka, J., 1969, Models for Modalities , Reidel, Dordrecht