Merging and testing opinions

TL;DR

This paper explores how opinions can be merged and tested within a prediction model, revealing the influence of incentives on the ability to reject opinions and establishing links between opinion merging, testing, and Bayesian learning.

Contribution

It demonstrates the relationship between opinion testing and merging, especially highlighting how incentives affect the rejection of opinions in Bayesian settings.

Findings

Opinions can be tested and rejected without incentives if data leads to consensus.

With incentives, opinions can only be tested when consensus exists.

The study links Bayesian learning with strategic expert testing.

Abstract

We study the merging and the testing of opinions in the context of a prediction model. In the absence of incentive problems, opinions can be tested and rejected, regardless of whether or not data produces consensus among Bayesian agents. In contrast, in the presence of incentive problems, opinions can only be tested and rejected when data produces consensus among Bayesian agents. These results show a strong connection between the testing and the merging of opinions. They also relate the literature on Bayesian learning and the literature on testing strategic experts.