Contrarian Motives in Social Learning

Abstract

We study sequential social learning with endogenous information acquisition when agents have a taste for nonconformity. Each agent observes predecessors' actions, chooses whether to acquire a private signal (and its precision), and then selects between two actions. Payoffs reward correctness and add a history-based bonus for taking the less popular action, so equilibrium inference remains Bayesian without fixed points in anticipated popularity. In a Gaussian-quadratic specification, optimal actions are posterior thresholds that shift linearly with observed popularity and contrarian intensity, tilting decisions against the majority. We solve the precision choice problem with a fixed entry cost and a convex cost of precision. Whenever the no-signal action coincides with the observed majority, stronger contrarian motives weakly increase the maximized value of information and enlarge the set of histories in which agents invest in signals. We also derive comparative statics for thresholds and choice probabilities. In particular, increasing contrarian intensity reduces the likelihood of taking the currently popular action in both states.