Bias Detection Via Signaling

TL;DR

This paper proposes a method to detect bias in agents' belief updating by designing signals and observing actions, providing theoretical characterizations and efficient algorithms for minimal signaling.

Contribution

It introduces a novel framework for bias detection using signaling schemes and characterizes when a single signal suffices, along with an efficient computation method.

Findings

Single-signal sufficiency characterized for certain scenarios

Efficient algorithm developed for optimal signaling schemes

Framework applicable to belief bias detection in various settings

Abstract

We introduce and study the problem of detecting whether an agent is updating their prior beliefs given new evidence in an optimal way that is Bayesian, or whether they are biased towards their own prior. In our model, biased agents form posterior beliefs that are a convex combination of their prior and the Bayesian posterior, where the more biased an agent is, the closer their posterior is to the prior. Since we often cannot observe the agent's beliefs directly, we take an approach inspired by information design. Specifically, we measure an agent's bias by designing a signaling scheme and observing the actions they take in response to different signals, assuming that they are maximizing their own expected utility; our goal is to detect bias with a minimum number of signals. Our main results include a characterization of scenarios where a single signal suffices and a computationally efficient algorithm to compute optimal signaling schemes.