AI

Preemptive Policy Experimentation

Abstract

We develop a model of experimentation and learning in policymaking when control of power is temporary. We demonstrate how an early office holder who would otherwise not experiment is nonetheless induced to experiment when his hold on power is temporary. This preemptive policy experiment is profitable for the early office holder as it reveals information about the policy mapping to his successor, information that shapes future policy choices. Thus policy choices today can cast a long shadow over future choices purely through information transmission and absent any formal institutional constraints or real state variables. The model we develop utilizes a recent innovation that represents the policy mapping as the realized path of a Brownian motion. We provide a precise characterization of when preemptive experimentation emerges in equilibrium and the form it takes. We apply the model to several well known episodes of policymaking, reinterpreting the policy choices as preemptive experiments.