Authors
Federica Russo
University of Amsterdam
Jon Williamson
University of Kent
Abstract
The Recursive Bayesian Net (RBN) 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
Keywords Analytic Philosophy  Philosophy of Science
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ISBN(s) 0495-4548  
DOI theoria20112611
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Constitutive Relevance, Mutual Manipulability, and Fat-Handedness.Michael Baumgartner & Alexander Gebharter - 2016 - British Journal for the Philosophy of Science 67 (3):731-756.
A Formal Framework for Representing Mechanisms?Alexander Gebharter - 2014 - Philosophy of Science 81 (1):138-153.
Causal Graphs and Biological Mechanisms.Alexander Gebharter & Marie I. Kaiser - 2014 - In Marie I. Kaiser, Oliver Scholz, Daniel Plenge & Andreas Hüttemann (eds.), Explanation in the special sciences: The case of biology and history. Dordrecht: Springer. pp. 55-86.
How to Model Mechanistic Hierarchies.Lorenzo Casini - 2016 - Philosophy of Science 83 (5):946-958.

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