This paper compares two prominent approaches in artificial language evolution: Iterated Learning and Social Coordination. More specifically, the paper contrasts experiments in both approaches on how populations of artificial agents can autonomously develop a grammatical case marking system for indicating event structure (i.e. ?who does what to whom?). The comparison demonstrates that only the Social Coordination approach leads to a shared communication system in a multi-agent population. The paper concludes with an analysis and discussion of the results, and argues that Iterated Learning in its current form cannot explain the emergence of more complex natural language-like phenomena.