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.Comment: Published in at http://dx.doi.org/10.1214/14-AOS1212 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org