Repository URL:
http://philsci-archive.pitt.edu/id/eprint/11167
Author(s):
Lorenzo Casini
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conference paper description
Mechanisms are usually viewed as inherently hierarchical, with lower levels of a mechanism influencing, and decomposing, its higher-level behaviour. In order to adequately draw quantitative predictions from a model of a mechanism, the model needs to capture this hierarchical aspect. The recursive Bayesian network (RBN) formalism was put forward as a means to model mechanistic hierarchies (Casini et al., 2011) by decomposing variables. The proposal was recently criticized by Gebharter (2014) and Gebharter and Kaiser (2014), who instead propose to decompose arrows. In this paper, I defend the RBN account from the criticism and argue that it offers a better representation of mechanistic hierarchies than the rival account.

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