David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Philosophy of Science 74 (3):304-329 (2007)
Like other mathematically intensive sciences, economics is becoming increasingly computerized. Despite the extent of the computation, however, there is very little true simulation. Simple computation is a form of theory articulation, whereas true simulation is analogous to an experimental procedure. Successful computation is faithful to an underlying mathematical model, whereas successful simulation directly mimics a process or a system. The computer is seen as a legitimate tool in economics only when traditional analytical solutions cannot be derived, i.e., only as a purely computational aid. We argue that true simulation is seldom practiced because it does not fit the conception of understanding inherent in mainstream economics. According to this conception, understanding is constituted by analytical derivation from a set of fundamental economic axioms. We articulate this conception using the concept of economists' perfect model. Since the deductive links between the assumptions and the consequences are not transparent in ‘bottom‐up’ generative microsimulations, microsimulations cannot correspond to the perfect model and economists do not therefore consider them viable candidates for generating theories that enhance economic understanding.
|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
Philip Kitcher (1993). The Advancement of Science: Science Without Legend, Objectivity Without Illusions. Oxford University Press.
Paul Humphreys (2004). Extending Ourselves Computational Science, Empiricism, and Scientific Method. Monograph Collection (Matt - Pseudo).
Henk W. De Regt & Dennis Dieks (2005). A Contextual Approach to Scientific Understanding. Synthese 144 (1):137 - 170.
J. D. Trout (2002). Scientific Explanation and the Sense of Understanding. Philosophy of Science 69 (2):212-233.
D. M. Hausman (1992). The Inexact and Separate Science of Economics. Cambridge, Cambridge University Press.
Citations of this work BETA
Julian Reiss (2012). The Explanation Paradox. Journal of Economic Methodology 19 (1):43-62.
J. Kuorikoski, A. Lehtinen & C. Marchionni (2010). Economic Modelling as Robustness Analysis. British Journal for the Philosophy of Science 61 (3):541-567.
Mary S. Morgan & Till Grüne-Yanoff (2013). Modeling Practices in the Social and Human Sciences. An Interdisciplinary Exchange. Perspectives on Science 21 (2):143-156.
Igor Douven (2010). Simulating Peer Disagreements. Studies in History and Philosophy of Science Part A 41 (2):148-157.
Similar books and articles
Stephan Hartmann (1996). The World as a Process: Simulations in the Natural and Social Sciences. In Rainer Hegselmann (ed.), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View.
Wendy S. Parker (2008). Computer Simulation Through an Error-Statistical Lens. Synthese 163 (3):371 - 384.
Alvin I. Goldman & Chandra S. Sripada (2005). Simulationist Models of Face-Based Emotion Recognition. Cognition 94 (3):193-213.
Jordi Fernández (2003). Explanation by Computer Simulation in Cognitive Science. Minds and Machines 13 (2):269-284.
Matthew W. Parker (2009). Computing the Uncomputable; or, the Discrete Charm of Second-Order Simulacra. Synthese 169 (3):447 - 463.
Johannes Lenhard (2006). Surprised by a Nanowire: Simulation, Control, and Understanding. Philosophy of Science 73 (5):605-616.
Ronald N. Giere (2009). Is Computer Simulation Changing the Face of Experimentation? Philosophical Studies 143 (1):59 - 62.
Added to index2009-01-28
Total downloads37 ( #88,037 of 1,725,574 )
Recent downloads (6 months)3 ( #211,098 of 1,725,574 )
How can I increase my downloads?