  We study whether individuals can learn the informativeness of their
information technology through social learning. As in the classic sequential
social learning model, rational agents arrive in order and make decisions based
on the past actions of others and their private signals. There is uncertainty
regarding the informativeness of the common signal-generating process. We show
that learning in this setting is not guaranteed, and depends crucially on the
relative tail distributions of private beliefs induced by uninformative and
informative signals. We identify the phenomenon of perpetual disagreement as
the cause of learning and characterize learning in the canonical Gaussian
environment.
