Attitude Polarization

Psychological evidence suggests that people's learning behavior is often prone to a 'myside bias' or 'irrational belief persistence' in contrast to learning behavior exclusively based on objective data. In the context of Bayesian learning such a bias may result in diverging posterior beliefs and attitude polarization even if agents receive identical information. Such patterns cannot be explained by thestandard model of rational Bayesian learning that implies convergent beliefs. As our key contribution, we therefore develop formal models of Bayesian learning with psychological bias as alternatives to rational Bayesian learning. We derive conditions under which beliefs may diverge in the learning process and thus conform with the psychological evidence. Key to our approach is the assumption of ambiguous beliefs that are formalized as non-additive probability measures arising inChoquet expected utility theory. As a speciffc feature of our approach, our models of Bayesian learning with psychological bias reduce to rational Bayesian learning in the absence of ambiguity