Abstract

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 formalism was put forward as a means to model mechanistic hierarchies by decomposing variables. The proposal was recently criticized by Gebharter and Gebharter and Kaiser, 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.