Contrarian Motives in Social Learning: Information Cascades with Nonconformist Preferences

Georgy Lukyanov () and Vasilii Ivanik

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Abstract: We embed a taste for nonconformism into a canonical Bikhchandani-Hirshleifer-Welch social-learning model. Agents value both correctness and choosing the minority action (fixed or proportion-based bonus). We study exogenous signals and endogenous acquisition with a fixed entry cost and convex cost of precision in a Gaussian-quadratic specification. Contrarian motives shift equilibrium cutoffs away from 1/2 and expand the belief region where information is purchased, sustaining informative actions; conditional on investing, chosen precision is lower near central beliefs. Welfare is shaped by a trade-off: mild contrarianism counteracts premature herding, whereas strong contrarianism steers actions against informative social signals and induces low-value experimentation. A tractable characterization delivers closed-form cutoffs, comparative statics, and transparent welfare comparisons. Applications include scientific priority races and academic diffusion, where distinctiveness yields rents yet excessive contrarianism erodes information aggregation.

Date: 2025-08, Revised 2025-09
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