In-depth studies of sociotechnical systems are largely limited to single instances. Network surveys are expensive, and platforms vary in important ways, from interface design, to social norms, to historical contingencies. With single examples, we can not in general know how much of observed network structure is explained by historical accidents, random noise, or meaningful social processes, nor can we claim that network structure predicts outcomes, such as organization success or ecosystem health. Here, I show how we can adopt a comparative approach for settings where we have, or can cleverly construct, multiple instances of a network to estimate the natural variability in social systems. The comparative approach makes previously untested theories testable. Drawing on examples from the social networks literature, I discuss emerging directions in the study of populations of sociotechnical systems using insights from organization theory and ecology.
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