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.

Another problem with RBN models of mechanisms

Gebharter Alexander
Primo
Writing – Original Draft Preparation
2016

Abstract

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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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/306250
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