Models for Prediction, Explanation and Control

Theoria 26 (1):5-33 (2011)
Abstract
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 tomodel 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
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Jon Williamson (2011). Models for Prediction, Explanation and Control: Recursive Bayesian Networks. Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 26 (70):5-33.
Phyllis McKay Illari & Jon Williamson (2011). Mechanisms Are Real and Local. In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oup Oxford.
Gregory Wheeler & Richard Scheines (2011). Causation, Association and Confirmation. In Stephan Hartmann, Marcel Weber, Wenceslao Gonzalez, Dennis Dieks & Thomas Uebe (eds.), Explanation, Prediction, and Confirmation: New Trends and Old Ones Reconsidered. Springer. 37--51.
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