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How to Model Mechanistic Hierarchies

Casini, Lorenzo (2014) How to Model Mechanistic Hierarchies. In: UNSPECIFIED.

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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 (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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Item Type: Conference or Workshop Item (UNSPECIFIED)
Creators:
CreatorsEmailORCID
Casini, Lorenzolorenzo.casini@unige.ch
Keywords: mechanism, causality, constitution, explanation, intervention, recursive Bayesian network, multilevel causal model
Subjects: Specific Sciences > Biology
General Issues > Causation
General Issues > Explanation
General Issues > Models and Idealization
Depositing User: Dr. Lorenzo Casini
Date Deposited: 25 Nov 2014 17:09
Last Modified: 25 Nov 2014 17:09
Item ID: 11167
Subjects: Specific Sciences > Biology
General Issues > Causation
General Issues > Explanation
General Issues > Models and Idealization
Date: 4 November 2014
URI: https://philsci-archive.pitt.edu/id/eprint/11167

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