Another problem with RBN models of mechanisms

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THEORIA. An International Journal for Theory, History and Foundations of Science, ISSN: 0495-4548, Vol: 31, Issue: 2, Page: 177-188

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Alexander Gebharter
UPV/EHU Press, Euskal Herriko Unibertsitatea / Universidad del PaĆ­s Vasco
Arts and Humanities
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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 modeling 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 the effects of manipulation and control across the levels of a mechanism. 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 for many RBN models of mechanisms.

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