On attitude polarization under Bayesian learning with non-additive beliefs

Ample 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 the standard model of rational Bayesian learning that implies convergent beliefs. Based on Choquet expected utility theory, 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 despite the fact that all agents observe the same sample drawn from an i.i.d. process. Key to our approach is the description of ambiguous beliefs as neo-additive capacities (Chateauneuf et al., J Econ Theory 137:538–567, 2007), which allows for a flexible and parsimonious parametrization of departures from additive probability measures.