  In this study, I present a theoretical social learning model to investigate
how confirmation bias affects opinions when agents exchange information over a
social network. Hence, besides exchanging opinions with friends, agents observe
a public sequence of potentially ambiguous signals and interpret it according
to a rule that includes confirmation bias. First, this study shows that
regardless of level of ambiguity both for people or networked society, only two
types of opinions can be formed, and both are biased. However, one opinion type
is less biased than the other depending on the state of the world. The size of
both biases depends on the ambiguity level and relative magnitude of the state
and confirmation biases. Hence, long-run learning is not attained even when
people impartially interpret ambiguity. Finally, analytically confirming the
probability of emergence of the less-biased consensus when people are connected
and have different priors is difficult. Hence, I used simulations to analyze
its determinants and found three main results: i) some network topologies are
more conducive to consensus efficiency, ii) some degree of partisanship
enhances consensus efficiency even under confirmation bias and iii)
open-mindedness (i.e. when partisans agree to exchange opinions with opposing
partisans) might inhibit efficiency in some cases.
