期刊名称：THEORIA. An International Journal for Theory, History and Foundations of Science
印刷版ISSN：2171-679X
出版年度：2016
卷号：31
期号：2
页码：177-188
DOI：10.1387/theoria.14502
语种：English
出版社：UPV/EHU - University of the Basque Country
摘要：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.
其他摘要：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.
