Faculty of Economics and Business Administration Publications Database

On attitude polarization under Bayesian learning with non-additive beliefs

Authors:
Zimper, Alexander
Source:
Volume: 39
Number: 2
Pages: 181 - 212
Month: October
ISSN-Print: 0895-5646
Link External Source: Online Version
Year: 2009
Keywords: Non-additive Probability Measures; Choquet Expected Utility Theory; Bayesian Learning; Bounded Rationality
Abstract:

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. 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 despite the fact that all agents observe the same arbitrarily large sample, which is drawn from an "objective" i.i.d. process. Furthermore, one of our learning scenarios results in attitude polarization even in the case of common priors. Key to our approach is the assumption of ambiguous beliefs that are formalized as non-additive probability measures arising in Choquet expected utility theory. More precisely, we focus on neo-additive capacities (Chateauneuf et. al. 2007) as a flexible and parsimonious
parametrization of departures from additive probability measures. As a specific feature of our approach, our models of Bayesian learning with psychological bias reduce to rational Bayesian learning in the absence of ambiguity.

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