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How to Model Mechanistic Hierarchies

Casini, Lorenzo (2015) How to Model Mechanistic Hierarchies. In: UNSPECIFIED.

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Abstract

Mechanisms are usually viewed as 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 into their constituting causal networks. The proposal was criticized by Gebharter (2014). He proposes an alternative formalism, which decomposes arrows. Here, I defend RBNs from the criticism and argue that they offer 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: 16 Jun 2015 20:02
Last Modified: 13 Sep 2015 15:57
Item ID: 11168
Subjects: Specific Sciences > Biology
General Issues > Causation
General Issues > Explanation
General Issues > Models and Idealization
Date: 14 June 2015
URI: https://philsci-archive.pitt.edu/id/eprint/11168

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