Abstract In a multiagent system, a (social) norm describes what the agents may expect from each other. Norms promote autonomy (an agent need not comply with a norm) and heterogeneity (a norm describes interactions at a high level independent of implementation details). Researchers have studied norm emergence through social learning where the agents interact repeatedly in a graph structure. In contrast, we consider norm emergence in an open system, where membership can change, and where no predetermined graph structure exists. We propose Silk, a mechanism wherein a generator monitors interactions among member agents and recommends norms to help resolve conflicts. Each member decides on whether to accept or reject a recommended norm. Upon exiting the system, a member passes its experience along to incoming members of the same type. Thus, members develop norms in a hybrid manner to resolve conflicts. We evaluate Silk via simulation in the traffic domain. Our results show that social norms promoting conflict resolution emerge in both moderate and selfish societies via our hybrid mechanism.