Learning from Polls During Electoral Campaigns

Voters’ beliefs about the strength of political parties are a central part of many foundational political science theories. In this article, we present a dynamic Bayesian learning model that allows us to study how voters form these beliefs by learning from pre-election polls over the course of an election campaign. In the model, belief adaptation to new polls can vary due to the perceived precision of the poll or the reliance on prior beliefs. We evaluate the implications of our model using two experiments. We find that respondents update their beliefs assuming that the polls are relatively imprecise but still weigh them more strongly than their priors. Studying implications for motivational learning by partisans, we find that varying adaptation works through varying reliance on priors and not necessarily by discrediting a poll’s precision. The findings inform our understanding of the consequences of learning from polls during political campaigns and motivational learning in general.