Authors
Alexander Gebharter
Ludwig Maximilians Universität, München
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 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 for many RBN models of mechanisms.
Keywords causation  causal Bayes net  mechanisms  modeling  prediction
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Reprint years 2016
ISBN(s) 0495-4548
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References found in this work BETA

Thinking About Mechanisms.Peter Machamer, Lindley Darden & Carl F. Craver - 2000 - Philosophy of Science 67 (1):1-25.
Explanation: A Mechanist Alternative.William Bechtel - 2005 - Studies in History and Philosophy of Biol and Biomed Sci 36 (2):421--441.
Explanation: A Mechanist Alternative.William Bechtel & Adele Abrahamsen - 2005 - Studies in History and Philosophy of Biological and Biomedical Sciences 36 (2):421-441.

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Citations of this work BETA

Alexander Gebharter: Causal Nets, Interventionism, and Mechanisms. Philosophical Foundations and Applications.Lorenzo Casini - 2018 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 49 (3):481-485.

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