Abstract

There is a long philosophical tradition of addressing questions in philosophy of science and epistemology by means of the tools of Bayesian probability theory (see Earman (1992) and Howson and Urbach (1993)). In the late '70s, an axiomatic approach to conditional independence was developed within a Bayesian framework. This approach in conjunction with developments in graph theory are the two pillars of the theory of Bayesian Networks, which is a theory of probabilistic reasoning in artificial intelligence. The theory has been very successful over the last two decades and has found a wide array of applications ranging from medical diagnosis to safety systems for hazardous industries. Aside from some excellent work in the theory of causation (see Pearl (2000) and Spirtes et al. (2001)), philosophers have been sadly absent in reaping the fruits from these new developments in artificial intelligence. This is unfortunate, since there are some long-standing questions in philosophy of science and epistemology in which the route to progress has been blocked by a type of complexity that is precisely the type of complexity that Bayesian Networks are designed to deal with: questions in which there are multiple variables in play and the conditional independences between these variables can be clearly identified. Integrating Bayesian Networks into philosophical research leads to theoretical advances on long-standing questions in philosophy and has a potential for practical applications.