 One result that computational modeling promises regarding a phenomenon such as opinion polarization is an understanding of the phenomenon itself: how real opinion polarization might happen, and how it might be avoided. Another and very different outcome, however, is created by the fact that computational modeling both offers and demands precision about concepts and measures that may otherwise be lacking in theory. Bramson et al. (2017), for example, argues that “polarization” has a range of possible meanings across the literature in which it appears, different aspects of which are captured by different computational models with different measures.