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How to Model Mechanistic Hierarchies

Published online by Cambridge University Press:  01 January 2022

Lorenzo Casini
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Abstract

Mechanisms are usually viewed as hierarchical, with lower levels of a mechanism influencing, and decomposing, its higher-level behavior. To draw quantitative predictions from a model of a mechanism, the model must capture this hierarchical aspect. Recursive Bayesian networks (RBNs) were put forward by Lorenzo Casini et al. as a means to model mechanistic hierarchies by decomposing variables into their constituting causal networks. The proposal was criticized by Alexander Gebharter. He proposes an alternative formalism, which instead decomposes arrows. Here, I defend RBNs from the criticism and argue that they offer a better representation of mechanistic hierarchies than the rival account.

Type
Adequacy of Causal Graphs and Bayes Networks
Information
Philosophy of Science , Volume 83 , Issue 5 , December 2016 , pp. 946 - 958
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

†

I thank the Lake Geneva Biological Interest Group and the audience of the Philosophy of Science Association symposium in Chicago, November 6–8, 2014, where this article was presented. I am especially grateful to Michael Baumgartner, Alexander Gebharter, Guillaume Schlaepfer, and Jon Williamson. This work was supported by the Swiss National Science Foundation (grant CRSII 1_147685/1).

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