Can Good Policies be Found? Repeated Elections, Learning, and Policy Dynamics

I study a model of dynamic policy making in which candidates and voters do not have complete knowledge of how the policy process works. Citizens learn through repeated elections as policies are implemented and outcomes observed. At each point citizens face a basic trade-off: Should they settle for a known but less-than-perfect policy, or should they experiment with policy in the hope of improving the outcome but at the risk of making things worse. I characterize the policy trajectory for this environment with myopic voters. The trajectory is path-dependent as policies are experimented with, learned from, and discarded. Yet basic patterns emerge. I show that policy making passes sequentially through three phases: a monotonic phase, a triangulating phase, and ultimately a stable phase. Policy making generally stabilizes at outcomes close to the median voter's preference, although I show that it can at times get "stuck," in which case any outcome may prove stable.