Modelling mechanisms with causal cycles
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.
KeywordsBayesian nets Recursive Bayesian nets Cyclic causality Mechanisms Feedback Causal models Causation Mechanistic modelling
We would like to thank Lorenzo Casini, George Darby, Phyllis Illari, Mike Joffe, Federica Russo, and Michael Wilde for their helpful comments on this paper. Jon Williamson’s research is supported by the UK Arts and Humanities Research Council. Bert Leuridan is Postdoctoral Fellow of the Research Foundation—Flanders (FWO).
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