David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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In several accounts of what models are and how they function a specific view dominates. This view contains the following characteristics. First, there is a clear-cut distinction between theories, models and data and secondly, empirical assessment takes place after the model is built. This view in which discovery and justification are disconnected is not in accordance with several practices of mathematical business-cycle model building. What these practices show is that models have to meet implicit criteria of adequacy, such as satisfying theoretical, mathematical and statistical requirements, and be useful for policy. In order to be adequate, models have to integrate enough items to satisfy such criteria. These items include besides theoretical notions, policy views, mathematisations of the cycle and metaphors also empirical data and facts. So, the main thesis of this chapter is that the context of discovery is the successful integration of those items that satisfy the criteria of adequacy. Because certain items are empirical data and facts, justification can be built-in.
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Citations of this work BETA
Tarja Knuuttila (2011). Modelling and Representing: An Artefactual Approach to Model-Based Representation. Studies in History and Philosophy of Science 42 (2):262-271.
Mary S. Morgan (2001). Models, Stories and the Economic World. Journal of Economic Methodology 8 (3):361-384.
Isabelle Peschard (2011). Making Sense of Modeling: Beyond Representation. [REVIEW] European Journal for Philosophy of Science 1 (3):335-352.
Axel Gelfert (2011). Mathematical Formalisms in Scientific Practice: From Denotation to Model-Based Representation. Studies in History and Philosophy of Science 42 (2):272-286.
Isabelle F. Peschard & Bas C. van Fraassen (2014). Making the Abstract Concrete: The Role of Norms and Values in Experimental Modeling. Studies in History and Philosophy of Science Part A 46:3-10.
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