Reasons as Causes in Bayesian Epistemology

Correct reasoning accords with Bayesian principles is now so widely held in philosophy, psychology, computer science, and elsewhere that the contrary is beginning to seem obtuse, or at best quaint. And that rational agents should learn about the world from energies striking sensory inputs--nerves in people--seems beyond question. To square these views with the demands of computability, Glymour and Danks explore on a particular formalization, causal Bayes nets, as an account of human reasoning about and to causal connections. Such structures can be used by rational agents, including humans in so far as they are rational, to have degrees of belief in various conceptual contents, which they use to reason to expectations, which are realized or defeated by sensory inputs, which cause them to change their degrees of belief in other contents in accord with Bayes's Rule, or some generalization of it.