Experiments as convex functions: three applications

Abstract

When the state space is binary or when only posterior means matter, it is possible to represent any experiment (joint distribution of truth and data) as a convex function. I present three applications of this representation: (i) manipulating posterior means, (ii) determining joint informational content of multiple datasets, and (iii) rationalizing reactions to unknowns sources of information.