How and when do political actors make policy choices in a complex world?  More specifically, when do state political actors decide their status quo is fails to meet their needs and then reformulate their policy?  When is this change linked to what other states have done and which states provide valuable information?  The purpose of this paper is to compare and contrast mimicry and learning and then assess the usefulness of one existing model of innovation choices based upon learning for deriving empirically testable hypotheses about state policy innovation and diffusion patterns.  The empirical implications of the formal model and limitations are also considered in a dataset of state newborn screening and abortion policy choices from 1965 through 2014.