David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
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.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
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.
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 Peschard & Bas van Fraassen (2014). Making the Abstract Concrete: The Role of Norms and Values in Experimental Modeling. Studies in History and Philosophy of Science 46:3-10.
Grant Fisher (2006). The Autonomy of Models and Explanation: Anomalous Molecular Rearrangements in Early Twentieth-Century Physical Organic Chemistry. Studies in History and Philosophy of Science Part A 37 (4):562-584.
Similar books and articles
Johannes Lenhard (2006). Models and Statistical Inference: The Controversy Between Fisher and Neyman–Pearson. British Journal for the Philosophy of Science 57 (1):69-91.
Katherine Dunlop (2009). Why Euclid's Geometry Brooked No Doubt: J. H. Lambert on Certainty and the Existence of Models. Synthese 167 (1):33 - 65.
Axel Gelfert, Simulating Many-Body Models in Physics: Rigorous Results, 'Benchmarks', and Cross-Model Justification.
Axel Gelfert (2009). Rigorous Results, Cross-Model Justification, and the Transfer of Empirical Warrant: The Case of Many-Body Models in Physics. Synthese 169 (3):497 - 519.
Marcel Boumans (2012). Measurement in Economics. In Uskali Mäki, Dov M. Gabbay, Paul Thagard & John Woods (eds.), Philosophy of Economics. North Holland. 395.
Jan M. Zytkow & Herbert A. Simon (1988). Normative Systems of Discovery and Logic of Search. Synthese 74 (1):65 - 90.
Andrew M. Yuengert (2006). Model Selection and Multiple Research Goals: The Case of Rational Addiction. Journal of Economic Methodology 13 (1):77-96.
Thomas J. Dohmen (2002). Building and Using Economic Models: A Case Study Analysis of the IS-LL Model. Journal of Economic Methodology 9 (2):191-212.
Added to index2009-07-19
Total downloads36 ( #54,872 of 1,413,458 )
Recent downloads (6 months)3 ( #67,796 of 1,413,458 )
How can I increase my downloads?