Contrarian Motives in Social Learning: Information Cascades with Nonconformist Preferences

We study sequential social learning with endogenous information acquisition when agents have a taste for nonconformity. Each agent observes predecessors' actions, decides whether to acquire a private signal (and how precise it should be), and then chooses between two actions. Payoffs value correctness and include a bonus for taking the less popular action among predecessors; because this bonus depends only on observed popularity, the equilibrium analysis avoids fixed points in anticipated popularity and preserves standard Bayesian updating. In a Gaussian-quadratic setting, optimal actions follow posterior thresholds that tilt against the majority, and we solve the precision choice problem. Whenever the no-signal decision aligns with the observed majority, stronger contrarian motives weakly raise the value of information and expand the set of histories in which agents invest. We provide compact comparative statics for thresholds, action probabilities, and the precision argmax, a local welfare-and-information treatment, and applications to scientific priority races, cultural diffusion, and online platforms.