Scaling down

While “scaling up” is a lively topic in network science and Big Data analysis today, my purpose in this essay is to articulate an alternative problem, that of “scaling down,” which I believe will also require increased attention in coming years. “Scaling down” is the problem of how macro-level features of Big Data affect, shape, and evoke lower-level features and processes. I identify four aspects of this problem: the extent to which findings from studies of Facebook and other Big-Data platforms apply to human behavior at the scale of church suppers and department politics where we spend much of our lives; the extent to which the mathematics of scaling might be consistent with behavioral principles, moving beyond a “universal” theory of networks to the study of variation within and between networks; and how a large social field, including its history and culture, shapes the typical representations, interactions, and strategies at local levels in a text or social network.