David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
In W. K. Essler & M. Frauchiger (eds.), Representation, Evidence, and Justification: Themes From Suppes. Ontos Verlag 1--95 (2008)
Models are a principle instrument of modern science. They are built, applied, tested, compared, revised and interpreted in an expansive scientific literature. Throughout this paper, I will argue that models are also a valuable tool for the philosopher of science. In particular, I will discuss how the methodology of Bayesian Networks can elucidate two central problems in the philosophy of science. The first thesis I will explore is the variety-of-evidence thesis, which argues that the more varied the supporting evidence, the greater the degree of confirmation for a given hypothesis. However, when investigated using Bayesian methodology, this thesis turns out not to be sacrosanct. In fact, under certain conditions, a hypothesis receives more confirmation from evidence that is obtained from one rather than more instruments, and from evidence that confirms one rather than more testable consequences of the hypothesis. The second challenge that I will investigate is scientific theory change. This application highlights a different virtue of modeling methodology. In particular, I will argue that Bayesian modeling illustrates how two seemingly unrelated aspects of theory change, namely the (Kuhnian) stability of (normal) science and the ability of anomalies to over turn that stability and lead to theory change, are in fact united by a single underlying principle, in this case, coherence. In the end, I will argue that these two examples bring out some metatheoretical reflections regarding the following questions: What are the differences between modeling in science and modeling in philosophy? What is the scope of the modeling method in philosophy? And what does this imply for our understanding of Bayesianism?
|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
Stephan Hartmann & Luc Bovens, The Variety-of-Evidence Thesis and the Reliability of Instruments: A Bayesian-Network Approach.
Patrick Maher (1988). Prediction, Accommodation, and the Logic of Discovery. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1988:273 - 285.
Luc Bovens & Stephan Hartmann (2002). Bayesian Networks and the Problem of Unreliable Instruments. Philosophy of Science 69 (1):29-72.
Patrick Forber (2012). Modeling Scientific Evidence: The Challenge of Specifying Likelihoods. In Henk W. de Regt (ed.), Epsa Philosophy of Science: Amsterdam 2009. Springer 55--65.
Johannes Lenhard (2007). Computer Simulation: The Cooperation Between Experimenting and Modeling. Philosophy of Science 74 (2):176-194.
Richard M. Shiffrin (2010). Perspectives on Modeling in Cognitive Science. Topics in Cognitive Science 2 (4):736-750.
William Bechtel & Adele Abrahamsen (2010). Dynamic Mechanistic Explanation: Computational Modeling of Circadian Rhythms as an Exemplar for Cognitive Science. Studies in History and Philosophy of Science Part A 41 (3):321-333.
William M. Goodwin (forthcoming). Global Climate Modeling as Applied Science. Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie:1-12.
Chris Pincock (2011). Modeling Reality. Synthese 180:19-32.
Added to index2009-01-28
Total downloads41 ( #80,753 of 1,725,584 )
Recent downloads (6 months)7 ( #93,199 of 1,725,584 )
How can I increase my downloads?