Synthese

, Volume 191, Issue 8, pp 1651–1681

Modelling mechanisms with causal cycles

Article

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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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Science and Technology StudiesUniversity College LondonLondonUK
  2. 2.Centre for Logic and Philosophy of Science, Department of Philosophy and Moral ScienceGhent UniversityGentBelgium
  3. 3.Philosophy DepartmentUniversity of KentCanterburyUK

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