Distrust in Experts and the Origins of Disagreement

Abstract

Why do individuals interpret the same information differently? We propose that individuals follow Bayes' Rule when forming posteriors with one exception: when assessing the credibility of experts, they "double-dip" the data and use already-updated beliefs instead of their priors. Our proposed mistake predicts when individuals over- or underreact to new information, depending on the order in which they received previous signals. It explains why information that should objectively mitigate disagreement may amplify it instead. In a trading game application, traders engage in excessive speculation associated with bubbles and endogenous crashes. Our model provides a theory of the origins of disagreement: individuals disagree about both unknown states and credibility despite sharing common priors and information.

Keywords: disagreement, polarization, learning, speculation, bubbles

JEL Classification: D91, D83, G41