This talk is about a hybrid approach to knowledge acquisition and representation for machine ethics or, more generally, computational normative reasoning. Building on recent research in artificial intelligence and law, the approach is modeled on the familiar practice of decision-making under precedential constraint in the common law. I will first provide a formal characterization of this practice, showing how a body of normative information can be constructed in a way that is piecemeal, distributed, and responsive to particular circumstances. I will then discuss one possible application to a robot childminder, some open questions, and work in progress.