Modelling mechanisms with causal cycles

Synthese 191 (8):1-31 (2014)
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):5–33, 2011) put forward the Recursive Bayesian Networks (RBN) formalism as well suited to this end. The RBN formalism is an extension of the standard Bayesian net formalism, an extension that allows for modelling the hierarchical nature of mechanisms. 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 can be solved by combining two sorts of solution strategy in a judicious way
Keywords Bayesian nets  Recursive Bayesian nets  Cyclic causality  Mechanisms  Feedback  Causal models  Causation  Mechanistic modelling
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DOI 10.1007/s11229-013-0360-7
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References found in this work BETA
Causality: Models, Reasoning, and Inference.Judea Pearl - 2000 - Cambridge University Press.
Thinking About Mechanisms.Peter K. Machamer, Lindley Darden & Carl F. Craver - 2000 - Philosophy of Science 67 (1):1-25.
Explanation: A Mechanist Alternative.William Bechtel - 2005 - Studies in History and Philosophy of Biol and Biomed Sci 36 (2):421--441.

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Citations of this work BETA
Williamson on Gettier Cases and Epistemic Logic.Stewart Cohen & Juan Comesaña - 2013 - Inquiry : An Interdisciplinary Journal of Philosophy 56 (1):15-29.
Can Interventions Rescue Glennan’s Mechanistic Account of Causality?Lorenzo Casini - 2016 - British Journal for the Philosophy of Science 67 (4):1155-1183.

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