Learning traps

When constituents are uncertain about whether leaders' policy preferences align with their interests, policy choices generate a trade‐off between payoffs and information. Leaders can experiment with their preferred policy, but if the outcome is bad, constituents learn this and threaten office removal. I use a repeated model of political agency to analyze how this trade‐off shapes long‐lived, term‐unlimited leaders' policy choices. When there is uncertainty about the optimal policy for constituents, leaders implement “learning traps”: spatially intermediate policies that halt learning about what the best policy is. They experiment only when their preferences are very likely to be aligned with constituents, a contrast to the “gambling for resurrection” logic ubiquitous in the literature. I provide a case study that examines how the 1861 reforms ending Serfdom in Imperial Russia represented the logic of a “learning trap” and illustrate additional applications of the model to political contagion and economic policymaking.