Publication
Title
Modelling mechanisms with causal cycles
Author
Abstract
Mechanistic philosophy of science views a large part of scientific activity as engaged in modelling mechanisms. While science textbooks tend to offer qualitative models of mechanisms, there is increasing demand for models from which one can draw quantitative predictions and explanations. Casini et al. (Theoria 26(1):533, 2011) put forward the Recursive Bayesian Networks (RBN) formalism as well suited to this end. TheRBNformalism is an extension of the standardBayesian net formalism, an extension that allows formodelling the hierarchical nature ofmechanisms. Like the standard Bayesian net formalism, it models causal relationships using directed acyclic graphs. Given this appeal to acyclicity, causal cycles pose a prima facie problem for the RBN approach. This paper argues that the problem is a significant one given the ubiquity of causal cycles in mechanisms, but that the problem
Language
English
Source (journal)
Synthese : an international journal for epistemology, methodology and philosophy of science. - Dordrecht, 1936, currens
Publication
Dordrecht : 2014
ISSN
0039-7857
Volume/pages
191:8(2014), p. 1651-1681
ISI
000335505900001
Full text (Publisher's DOI)
Full text (open access)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
External links
Web of Science
Record
Identification
Creation 27.11.2014
Last edited 20.08.2017
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