The Contrarian Bias of Incentivizing Learning

Abstract Delegating high-stakes decisions creates a fundamental tension: incentivizing experts to acquire unobservable information inevitably distorts their final choices. In a principal-agent setting, we characterize the optimal compensation contract under hidden learning, showing it endogenously generates either contrarian or conformist bias. The direction of this bias depends on learning costs and the precision of public and private information. Our framework links information acquisition incentives to systematic biases in experts’ choices and offers a unifying explanation for conflicting empirical evidence in financial advice: why analysts issue excessive contrarian recommendations, and why inexperienced analysts follow the consensus more than their experienced peers.