Modelling mechanisms with causal cycles

Mechanistic philosophy of science views a large part of scientific activity as engaged in modelling mechanisms. While science textbooks tend to offer qualitative models of mechanisms, there is increasing demand for models from which one can draw quantitative predictions and explanations. Casini et al. (Theoria 26(1):533, 2011) put forward the Recursive Bayesian Networks (RBN) formalism as well suited to this end. TheRBNformalism is an extension of the standardBayesian net formalism, an extension that allows formodelling the hierarchical nature ofmechanisms. Like the standard Bayesian net formalism, it models causal relationships using directed acyclic graphs. Given this appeal to acyclicity, causal cycles pose a prima facie problem for the RBN approach. This paper argues that the problem is a significant one given the ubiquity of causal cycles in mechanisms, but that the problem

Jaar van publicatie:2014

Trefwoorden:A1 Journal article

Toegankelijkheid:Open