Weighted Garbling

TL;DR

This paper introduces a weighted garbling order for experiments, providing a new way to compare their informativeness based on posterior beliefs and decision-theoretic criteria.

Contribution

It generalizes the concept of garbling, characterizes the order via posterior support, and offers decision-theoretic characterizations in static and dynamic settings.

Findings

Weighted garbling order depends only on posterior support.

An experiment dominates another if it provides a higher value of information across problems.

Dominance in dynamic problems aligns with higher expected payoffs for all priors.

Abstract

We introduce an information order on experiments based on weighted garbling, a generalization of the standard notion of garbling. In this order, an experiment is more informative than another if the latter is a weighted garbling of the former. We show that this is equivalent to ordinary garbling conditional on a payoff-irrelevant event. We also characterize the order in terms of induced posterior belief distributions, showing that it depends only on their support. Our main results provide two decision-theoretic characterizations of this order. First, in static decision problems, one experiment dominates another if and only if its value of information is at least a fixed fraction of the other's across all problems. Second, in a class of stopping time problems with a hidden Markov process and repeated experimentation, one experiment dominates another if and only if it yields weakly higher expected payoffs for every problem with a regular prior.