One of the hallmarks of human natural language inter- action is the ability for people to balance a variety of so- cial and communicative goals when choosing how to realize their speech actions. These goals can include pragmatic criteria such as correctness, informativeness, and brevity (i.e., Gricean conversational maxims) or social factors such as politeness. However, there currently does not exist a general algorithmic method to explicitly modulate language generated by artificial agents based on an arbitrary number of pragmatic and social criteria. We propose a novel method to accomplish this task, in which rankings of candidate utterances by different prag- matic or social criteria are fused by use of a voting algorithm. We then give a proof-of-concept demonstration of the applica- tion of this method in the context of directive generation for human-robot interaction.