Social systems, and particularly infectious disease epidemiology, present serious challenges for evidence-based policy-making. Chief among them is drawing inferences from natural, as opposed to controlled, experiments in enormous configuration spaces where data are either unavailable, incommensurate, or collected for other purposes. This talk will explore whether and how ideas about measurement, representational complexity, sensitivity analysis, and even consistency translate from data- or model-rich environments like physics, finance, and linguistics to social systems.