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, 31(2), 177–188. https://doi.org/10.1387/theoria.14502
recursive Bayesian networks, mechanism, modelling, intervention, manipulation, control
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons License.