Confirmation Bias in Social Networks

I propose a social learning model that investigates how confimatory bias affects public opinion when agents exchange information over a social network. For that, besides exchanging opinions with friends, individuals observe a public sequence of potentially ambiguous signals and they interpret it according to a rule that accounts for confirmation bias. I first show that, regardless the level of ambiguity and both in the case of a single individual or of a networked society, only two types of opinions might be formed and both are biased. One opinion type, however, is necessarily less biased (more efficient) than the other depending on the state of the world. The size of both biases depends on the ambiguity level and the relative magnitude of the state and confirmatory biases. In this context, long-run learning is not attained even when individuals interpret ambiguity impartially. Finally, since it is not trivial to ascertain analytically the probability of emergence of the efficient consensus when individuals are connected through a social network and have different priors, I use simulations to analyze its determinants. Three main results derived from this exercise are that, in expected terms, i) some network topologies are more conducive to consensus efficiency, ii) some degree of partisanship enhances consensus efficiency even under confirmatory bias and iii) open-mindedness, i.e. when partisans agree to exchange opinions with other partisans with polar opposite beliefs, might harm efficiency in some cases.