David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Journal of Economic Methodology 12 (3):407-431 (2005)
Nancy Cartwright views models as blueprints for nomological machines ? machines that, if properly shielded, generate law?like behaviour or regularities. Marcel Boumans has argued that we can look for devices inside models, which enable us to measure aspects of these regularities. Therefore, if models do produce regular behaviour (Cartwright), they might perhaps generate numbers about phenomena in the world, provided we can locate a good measuring device in the model (Boumans). How do they do this? Models are often understood to consist of internal principles and bridge principles. Both of these play a role in the measuring process. This paper suggests that we can understand the internal principles to be responsible for generating the regularities displayed by the model ? and for many users of the model, this is sufficient use. The bridge principles (following Cartwright ? ?the real bridge principles?) are a matter of picking the right mathematical representation to make the model work with respect to some real world case at hand. These ?real? bridge principles may enable the model to generate numbers and so serve to make the model into a measuring device. This paper explores this construction of a measuring device of duration dependence of unemployment. Since search theory cannot be made operational for this purpose duration models, which model of the outflow process of unemployment with a Weibull function, are taken as a representation of reservation wage setting in search models. As I show, this Weibull function serves as a bridge principle to make a measure of the elasticity of response between unemployment benefit and duration of unemployment. It enables measurement to take place by modelling behaviour according to some assumptions, which operate as constraints. While this Weibull function serves this purpose, its assumptions are arbitrary rather than connected to theory, and measurements generated with this bridge principle turn out to be highly sensitive to its specification. The Weibull function does enable the model to function as a measuring device, but the lack of ability to make newly invoked unobservable variables operational makes it of dubious worth.
|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
No citations found.
Similar books and articles
Marcel Boumans (2005). Measurement Outside the Laboratory. Philosophy of Science 72 (5):850-863.
Alessandro Giordani & Luca Mari (2014). Modeling Measurement: Error and Uncertainty. In Marcel Boumans, Giora Hon & Arthur Petersen (eds.), Error and Uncertainty in Scientific Practice. Pickering & Chatto. 79-96.
Alessandro Giordani & Luca Mari (2012). Measurement, Models, and Uncertainty. IEEE Transactions on Instrumentation and Measurement 61 (8):2144 - 2152.
Marcel Boumans (2012). Measurement in Economics. In Uskali Mäki, Dov M. Gabbay, Paul Thagard & John Woods (eds.), Philosophy of Economics. North Holland. 395.
Nancy Cartwright (2009). If No Capacities Then No Credible Worlds. But Can Models Reveal Capacities? Erkenntnis 70 (1):45 - 58.
Gert de Cooman & Peter Walley (2002). A Possibilistic Hierarchical Model for Behaviour Under Uncertainty. Theory and Decision 52 (4):327-374.
Martijn Meeter, Janneke Jehee & Jaap Murre (2007). Neural Models That Convince: Model Hierarchies and Other Strategies to Bridge the Gap Between Behavior and the Brain. Philosophical Psychology 20 (6):749 – 772.
M. Fattorosi-Barnaba & G. Amati (1987). Modal Operators with Probabilistic Interpretations, I. Studia Logica 46 (4):383 - 393.
Marcel Boumans (2012). Modeling Strategies for Measuring Phenomena In-and Outside the Laboratory. In Henk W. de Regt (ed.), Epsa Philosophy of Science: Amsterdam 2009. Springer. 1--11.
Margaret Morrison (2009). Models, Measurement and Computer Simulation: The Changing Face of Experimentation. Philosophical Studies 143 (1):33 - 57.
Marcel Boumans (1999). Representation and Stability in Testing and Measuring Rational Expectations. Journal of Economic Methodology 6 (3):381-402.
Stephan Hartmann (1995). Models as a Tool for Theory Construction: Some Strategies of Preliminary Physics. In William Herfel et al (ed.), Theories and Models in Scientific Processes. Rodopi.
Added to index2012-02-20
Total downloads2 ( #373,004 of 1,140,267 )
Recent downloads (6 months)1 ( #142,694 of 1,140,267 )
How can I increase my downloads?