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Modelling mechanisms with causal cycles

(2014) SYNTHESE. 191(8). p.1651-1681
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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
BIOLOGICAL EXPLANATION, MARKOV CONDITION, BAYESIAN NETWORKS, SLEEP-APNEA, INDEPENDENCE, MODULARITY, WOODWARD, HAUSMAN, ACCOUNT, DISEASE, Bayesian nets, Recursive Bayesian nets, Cyclic causality, Mechanisms, Feedback, Causal models, Causation, Mechanistic modelling

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Citation

Please use this url to cite or link to this publication:

Chicago
Clarke, Brendan, Bert Leuridan, and Jon Williamson. 2014. “Modelling Mechanisms with Causal Cycles.” Synthese 191 (8): 1651–1681.
APA
Clarke, B., Leuridan, B., & Williamson, J. (2014). Modelling mechanisms with causal cycles. SYNTHESE, 191(8), 1651–1681.
Vancouver
1.
Clarke B, Leuridan B, Williamson J. Modelling mechanisms with causal cycles. SYNTHESE. 2014;191(8):1651–81.
MLA
Clarke, Brendan, Bert Leuridan, and Jon Williamson. “Modelling Mechanisms with Causal Cycles.” SYNTHESE 191.8 (2014): 1651–1681. Print.
@article{4413804,
  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.},
  author       = {Clarke, Brendan and Leuridan, Bert and Williamson, Jon},
  issn         = {0039-7857},
  journal      = {SYNTHESE},
  keyword      = {BIOLOGICAL EXPLANATION,MARKOV CONDITION,BAYESIAN NETWORKS,SLEEP-APNEA,INDEPENDENCE,MODULARITY,WOODWARD,HAUSMAN,ACCOUNT,DISEASE,Bayesian nets,Recursive Bayesian nets,Cyclic causality,Mechanisms,Feedback,Causal models,Causation,Mechanistic modelling},
  language     = {eng},
  number       = {8},
  pages        = {1651--1681},
  title        = {Modelling mechanisms with causal cycles},
  url          = {http://dx.doi.org/10.1007/s11229-013-0360-7},
  volume       = {191},
  year         = {2014},
}

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