A computational model of political attitudes and beliefs is developed that incorporates contemporary psychological theory with well-documented findings from electoral behavior. We compare this model, John Q. Public (JQP), to a Bayesian learning model via computer simulations of observed changes in candidate evaluations over the 2000 presidential campaign. In these simulations, JQP reproduces responsiveness, persistence, and polarization of political attitudes, while the Bayesian learning model has difficulty accounting for persistence and polarization. We conclude that “motivated reasoning”—the discounting of information that challenges priors along with the uncritical acceptance of attitude-consistent information—is the reason our model can better account for persistence and polarization in candidate evaluations.
Political Behavior – Springer Journals
Published: Oct 14, 2009
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