Authors
Jon Williamson
University of Kent
Federica Russo
University of Amsterdam
Phyllis Illari
University College London
Abstract
The Recursive Bayesian Net formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation and control respectively, an RBN can be applied to all these tasks. We show in particular how a simple two-level RBN can be used to model a mechanism in cancer science. The higher level of our model contains variables at the clinical level, while the lower level maps the structure of the cell's mechanism for apoptosis.
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References found in this work BETA

Explaining the Brain.Carl F. Craver - 2009 - Oxford University Press.
Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Cambridge University Press.
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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

Constitutive Relevance, Mutual Manipulability, and Fat-Handedness.Michael Baumgartner & Alexander Gebharter - 2016 - British Journal for the Philosophy of Science 67 (3):731-756.
How to Model Mechanistic Hierarchies.Lorenzo Casini - 2016 - Philosophy of Science 83 (5):946-958.
Can Interventions Rescue Glennan’s Mechanistic Account of Causality?Lorenzo Casini - 2016 - British Journal for the Philosophy of Science 67 (4):1155-1183.
Another Problem with RBN Models of Mechanisms.Alexander Gebharter - 2016 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 31 (2):177-188.

View all 7 citations / Add more citations

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