期刊名称：THEORIA. An International Journal for Theory, History and Foundations of Science
印刷版ISSN：2171-679X
出版年度：2011
卷号：26
期号：1
页码：5-33
DOI：10.1387/theoria.784
语种：English
出版社：UPV/EHU - University of the Basque Country
摘要：The Recursive Bayesian Net (RBN) formalism was originally developed for modelling nested causal relationships. In this paper we argue that the formalism can also be applied to modelling the hierarchical structure of mechanisms. The resulting network contains quantitative information about probabilities, as well as qualitative information about mechanistic structure and causal relations. Since information about probabilities, mechanisms and causal relations is vital for prediction, explanation and control respectively, an RBN can be applied to all these tasks. We show in particular how a simple two-level RBN can be used to model a mechanism in cancer science. The higher level of our model contains variables at the clinical level, while the lower level maps the structure of the cell's mechanism for apoptosis.
