Available online at www.sciencedirect.com
ScienceDirect Physics of Life Reviews 12 (2015) 26–27 www.elsevier.com/locate/plrev
Comment
Why interdisciplinary research enriches the study of crime Comment on “Statistical physics of crime: A review” by M.R. D’Orsogna and M. Perc Karsten Donnay a,b a Computational Social Science, ETH Zurich, Switzerland b Graduate Institute of International and Development Studies, Geneva, Switzerland
Received 24 December 2014; accepted 6 January 2015 Available online 14 January 2015 Communicated by L. Peliti
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 DOI of original article: http://dx.doi.org/10.1016/j.plrev.2014.11.001. E-mail address: [email protected]. http://dx.doi.org/10.1016/j.plrev.2015.01.011 1571-0645/© 2015 Elsevier B.V. All rights reserved.
K. Donnay / Physics of Life Reviews 12 (2015) 26–27
27
most explicit in the studies relying on self-excited Poisson models reviewed in Section 3. The research on gang and criminal networks discussed in Section 5 additionally illustrates how important it is to also take underlying (criminal) network structures and their endogenous evolution into account. In moving forward with this research agenda it is important though to keep a number of caveats and possible complications in mind. Simple abstract models are especially powerful to illustrate the importance of systemic dynamics and to derive high-level explanations of criminal behavior. For much of the day-to-day interest of stakeholders and policy makers, however, such models may often be too abstract and removed from the specific cases of interest. A logical extension to the research reviewed in [3] is therefore to combine the powerful analytical and computational techniques reviewed with the detailed conceptual and empirical insights on criminal behavior, which decades of research in the social sciences, in particular in criminology, have revealed. In other words, this research would further increase the degree at which it aligns new methodological approaches with existing theoretical and empirical insights. In many cases this may, of course, imply that studies would have to focus, for example, on specific cities to empirically test hypotheses on urban crime. The agent-based models reviewed by D’Orsogna and Perc may serve as an example. It is not only possible to refine behavioral rules such that the model mechanisms more closely represent the best to-date theoretical and empirical understanding of the specific drivers of crime in a given city. It is also feasible to directly seed models with contextual information on structural factors, including law enforcement activities, and specify the model topology such that computational agents interact directly on a realistic representation of their urban environment. This effectively endogenizes the effect of structural conditions on crime while at the same time allowing for rich systemic interactions. Note that these evidence-driven agent-based modeling techniques are thus also very powerful frameworks to empirically test relatively general mechanisms of crime without neglecting the detail and context of a specific urban setting. Recently they have, for example, already been successfully used to study urban violence [4] and could certainly be adapted for the study of crime. An important caveat for any quantitative study that uses empirical data to analyze spatiotemporal patterns of crime arises from the use of data-driven predictive policing by a growing number of law enforcement agencies worldwide. While predictive policing promises to ensure that policing can be better targeted to counter criminal activity, it also has important methodological implications: in essence, all data on crime in a given city which employs predictive policing will be closely linked to and systematically influenced by policing activities. The effect of policing on patterns of crime has, of course, always been relevant. The targeted interventions of predictive policing, however, will most certainly render it significantly more difficult to empirically disentangle the effect of structural conditions, the motivation of offenders, the activity of the police and the complex interactions among them. In other words, without a detailed knowledge of the day-by-day activities of police forces, quantitative studies will seriously suffer from the strong endogenous relationship of crime and policing. This applies to classical econometric analyses but also to the methodologies discussed in [3]. The research reviewed by D’Orsogna and Perc represents a promising young interdisciplinary research field. In order to more substantively contribute to the study of crime, the field would, however, certainly profit from an even closer engagement with the social science literature and with criminology in particular. This is especially relevant for studies that seek to provide directly policy relevant insights. Ultimately, social scientists will be most interested in new methodological approaches and would most easily recognize the full merit of the innovative research reviewed in [3] if studies were to actively address longstanding and central questions that are of broad interest in the field. Interdisciplinary collaborations here decisively matter: the closer the collaboration with subject experts from the social sciences the better. References [1] Lazer D, Pentland A, Adamic L, Aral S, Barabasi A, Brewer D, et al. Life in the network: the coming age of computational social science. Science (New York, NY) 2009;323(5915):721. [2] Giles J. Computational social science: making the links. Nature 2012;488(7412):448–50. [3] D’Orsogna MR, Perc M. Statistical physics of crime: a review. Phys Life Rev 2015;12:1–21 [in this issue]. [4] Bhavnani R, Donnay K, Miodownik D, Mor M, Helbing D. Group segregation and urban violence. Am J Polit Sci 2014;58(1):226–45.
ScienceDirect Physics of Life Reviews 12 (2015) 26–27 www.elsevier.com/locate/plrev
Comment
Why interdisciplinary research enriches the study of crime Comment on “Statistical physics of crime: A review” by M.R. D’Orsogna and M. Perc Karsten Donnay a,b a Computational Social Science, ETH Zurich, Switzerland b Graduate Institute of International and Development Studies, Geneva, Switzerland
Received 24 December 2014; accepted 6 January 2015 Available online 14 January 2015 Communicated by L. Peliti
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 DOI of original article: http://dx.doi.org/10.1016/j.plrev.2014.11.001. E-mail address: [email protected]. http://dx.doi.org/10.1016/j.plrev.2015.01.011 1571-0645/© 2015 Elsevier B.V. All rights reserved.
K. Donnay / Physics of Life Reviews 12 (2015) 26–27
27
most explicit in the studies relying on self-excited Poisson models reviewed in Section 3. The research on gang and criminal networks discussed in Section 5 additionally illustrates how important it is to also take underlying (criminal) network structures and their endogenous evolution into account. In moving forward with this research agenda it is important though to keep a number of caveats and possible complications in mind. Simple abstract models are especially powerful to illustrate the importance of systemic dynamics and to derive high-level explanations of criminal behavior. For much of the day-to-day interest of stakeholders and policy makers, however, such models may often be too abstract and removed from the specific cases of interest. A logical extension to the research reviewed in [3] is therefore to combine the powerful analytical and computational techniques reviewed with the detailed conceptual and empirical insights on criminal behavior, which decades of research in the social sciences, in particular in criminology, have revealed. In other words, this research would further increase the degree at which it aligns new methodological approaches with existing theoretical and empirical insights. In many cases this may, of course, imply that studies would have to focus, for example, on specific cities to empirically test hypotheses on urban crime. The agent-based models reviewed by D’Orsogna and Perc may serve as an example. It is not only possible to refine behavioral rules such that the model mechanisms more closely represent the best to-date theoretical and empirical understanding of the specific drivers of crime in a given city. It is also feasible to directly seed models with contextual information on structural factors, including law enforcement activities, and specify the model topology such that computational agents interact directly on a realistic representation of their urban environment. This effectively endogenizes the effect of structural conditions on crime while at the same time allowing for rich systemic interactions. Note that these evidence-driven agent-based modeling techniques are thus also very powerful frameworks to empirically test relatively general mechanisms of crime without neglecting the detail and context of a specific urban setting. Recently they have, for example, already been successfully used to study urban violence [4] and could certainly be adapted for the study of crime. An important caveat for any quantitative study that uses empirical data to analyze spatiotemporal patterns of crime arises from the use of data-driven predictive policing by a growing number of law enforcement agencies worldwide. While predictive policing promises to ensure that policing can be better targeted to counter criminal activity, it also has important methodological implications: in essence, all data on crime in a given city which employs predictive policing will be closely linked to and systematically influenced by policing activities. The effect of policing on patterns of crime has, of course, always been relevant. The targeted interventions of predictive policing, however, will most certainly render it significantly more difficult to empirically disentangle the effect of structural conditions, the motivation of offenders, the activity of the police and the complex interactions among them. In other words, without a detailed knowledge of the day-by-day activities of police forces, quantitative studies will seriously suffer from the strong endogenous relationship of crime and policing. This applies to classical econometric analyses but also to the methodologies discussed in [3]. The research reviewed by D’Orsogna and Perc represents a promising young interdisciplinary research field. In order to more substantively contribute to the study of crime, the field would, however, certainly profit from an even closer engagement with the social science literature and with criminology in particular. This is especially relevant for studies that seek to provide directly policy relevant insights. Ultimately, social scientists will be most interested in new methodological approaches and would most easily recognize the full merit of the innovative research reviewed in [3] if studies were to actively address longstanding and central questions that are of broad interest in the field. Interdisciplinary collaborations here decisively matter: the closer the collaboration with subject experts from the social sciences the better. References [1] Lazer D, Pentland A, Adamic L, Aral S, Barabasi A, Brewer D, et al. Life in the network: the coming age of computational social science. Science (New York, NY) 2009;323(5915):721. [2] Giles J. Computational social science: making the links. Nature 2012;488(7412):448–50. [3] D’Orsogna MR, Perc M. Statistical physics of crime: a review. Phys Life Rev 2015;12:1–21 [in this issue]. [4] Bhavnani R, Donnay K, Miodownik D, Mor M, Helbing D. Group segregation and urban violence. Am J Polit Sci 2014;58(1):226–45.
