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Another problem with RBN models of mechanisms

Gebharter, Alexander (2016) Another problem with RBN models of mechanisms. THEORIA. An International Journal for Theory, History and Foundations of Science, 31 (2). pp. 177-188. ISSN 2171-679X

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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 in most RBN models of mechanisms.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Gebharter, Alexanderalexander.gebharter@gmail.com
Additional Information: ISSN: 0495-4548 (print)
Keywords: recursive Bayesian networks; mechanism; modelling; intervention; manipulation; control
Subjects: General Issues > Explanation
General Issues > Models and Idealization
Depositing User: Unnamed user with email theoria@ehu.es
Date Deposited: 08 Jun 2016 19:49
Last Modified: 08 Jun 2016 19:49
Item ID: 12179
Journal or Publication Title: THEORIA. An International Journal for Theory, History and Foundations of Science
Publisher: Euskal Herriko Unibertsitatea / Universidad del País Vasco
Official URL: http://www.ehu.eus/ojs/index.php/THEORIA/article/v...
DOI or Unique Handle: 10.1387/theoria.14502
Subjects: General Issues > Explanation
General Issues > Models and Idealization
Date: May 2016
Page Range: pp. 177-188
Volume: 31
Number: 2
ISSN: 2171-679X
URI: https://philsci-archive.pitt.edu/id/eprint/12179

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