The past several years have seen a rapidly growing interest in the use of advanced quantitative methodologies and formalisms adapted from the natural sciences to study a broad range of social phenomena. The research field of computational social science [1,2], for example, uses digital artifacts of human online activity to cast a new light on social dynamics. Similarly, the studies reviewed by D’Orsogna and Perc showcase a diverse set of advanced quantitative techniques to study the dynamics of crime. Methods used range from partial differential equations and self-exciting point processes to agent-based models, evolutionary game theory and network science [3]. The research reviewed should be seen within the larger context of and complimentary to a growing trend in the social sciences towards the use of ever more advanced and refined quantitative methodologies. Much of this is driven by a generation of researchers that in addition to their substantive subject interest, embrace interdisciplinary work and possess advanced technical skills. These three key themes – subject relevance, interdisciplinarity, advanced methodologies – reappear throughout the studies reviewed by D’Orsogna and Perc. The review, for example, repeatedly highlights that research in this emerging field is inherently interdisciplinary: studies use methodologies adapted from other disciplines but explicitly engage with existing work on the mechanisms and dynamics of criminal behavior. This is an important and critical prerequisite for generating results that are relevant for a wider, social science audience. At the same time, the studies demonstrate that the methodologies used may, in fact, shed new light on a number of relevant aspects of criminal activity. The review further emphasizes the importance of systemic dynamics for the understanding of emerging patterns of crime. Historically, much of the research on crime has focused on the structural conditions for crime or on the motivations of individuals to engage in criminal activity. A more systemic perspective additionally emphasizes the relevance of the dynamics that arise from complex interactions – among individuals and with the environment – for the understanding of patterns of crime. This is nicely illustrated by a number of studies reviewed by D’Orsogna and Perc. The research discussed in Section 4, for example, shows that very simple mechanisms paired with complex systemic interactions may help elucidate the effect of policing on levels of crime. Similarly, a number of studies explicitly highlight the relevance of endogenous “feedback” effects for our understanding of criminal activity – this is