Abstract

Objective Bayesianism has been criticised for not allowing learning from experience: it is claimed that an agent must give degree of belief 12 to the next raven being black, however many other black ravens have been observed. I argue that this objection can be overcome by appealing to objective Bayesian nets, a formalism for representing objective Bayesian degrees of belief. Under this account, previous observations exert an inductive influence on the next observation. I show how this approach can be used to capture the Johnson–Carnap continuum of inductive methods, as well as the Nix–Paris continuum, and show how inductive influence can be measured. 1. Introduction2. The Problem3. Diagnosis4. Objective Bayesian Nets5. Resolution6. The Johnson–Carnap Continuum7. The Nix–Paris Continuum8. Linguistic Slack9. Frequencies and Degrees of Belief10. Conclusion.