An Objective Justification of Bayesianism

One of the fundamental problems of epistemology is to say when the
evidence in an agent's possession justifies the beliefs she holds. We
defend the Bayesian solution to this problem by appealing to the following
fundamental norm:

- Try to minimize inaccuracy in your beliefs.

In this paper, we make this norm mathematically precise in various ways.
We describe epistemic dilemmas that an agent might face if she attempts to
follow this norm, and we show that the only inaccuracy measures that do
not give rise to such dilemmas are the quadratic inaccuracy measures.
Finally, we derive the main tenets of Bayesianism from the relevant
mathematical versions of the norm to which this characterization of the
legitimate inaccuracy measures gives rise, but we also show that Jeffrey
conditionalization has to be replaced by a different method of update in
order for the norm to be satisfied.

(This is joint work with R. Pettigrew.)