Another problem with RBN models of mechanisms



Published 24-05-2016
Alexander Gebharter


Casini, Illari, Russo, and Williamson (2011) suggest to model mechanisms by means of recursive Bayesian networks (RBNs) and Clarke, Leuridan, and Williamson (2014) extend their modelling approach to mechanisms featuring causal feedback. One of the main selling points of the RBN approach should be that it provides answers to questions concerning manipulation and control. In this paper I demonstrate that the method to compute the effects of interventions the authors mentioned endorse leads to absurd results under the additional assumption of faithfulness, which can be expected to hold in most RBN models of mechanisms.

How to Cite

Gebharter, A. (2016). Another problem with RBN models of mechanisms. THEORIA. An International Journal for Theory, History and Foundations of Science, 31(2), 177–188.
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recursive Bayesian networks, mechanism, modelling, intervention, manipulation, control