Statistical physics of crime: A review
Abstract: Containing the spreading of crime in urban societies remains a major challenge. Empirical evidence suggests that, left unchecked, crimes may be recurrent and proliferate. On the other hand, eradicating a culture of crime may be difficult, especially under extreme social circumstances that impair the creation of a shared sense of social responsibility. Although our understanding of the mechanisms that drive the emergence and diffusion of crime is still incomplete, recent research highlights applied mathematics and methods of statistical physics as valuable theoretical resources that may help us better understand criminal activity. We review different approaches aimed at modeling and improving our understanding of crime, focusing on the nucleation of crime hotspots using partial differential equations, self-exciting point process and agent-based modeling, adversarial evolutionary games, and the network science behind the formation of gangs and large-scale organized crime. We emphasize that statistical physics of crime can relevantly inform the design of successful crime prevention strategies, as well as improve the accuracy of expectations about how different policing interventions should impact malicious human activity deviating from social norms. We also outline possible directions for future research, related to the effects of social and coevolving networks and to the hierarchical growth of criminal structures due to self-organization.
Summary (1 min read)
- This is particularly evident – even for microcriminality – in urban areas (in Europe and North America the percent of population living in urban areas is around 85%).
- And the role of mathematical, statistical, and physical models has been steadily increasing.
- Thus, the paper  appears to be extremely timely and useful.
- Generally speaking, models are often more descriptive than predictive, in the sense that it is not expected that they predict e.g. the number of burglaries or car thefts that will occur in a given district over a given period of time.
- Nevertheless, they can be instrumental in describing the mechanisms by which it can be foreseen that a concentration of crimes can appear in particular zones (hot spots), or the “contagion” that criminal behaviour can have on particular classes of individuals.
- This description can in turn suggest how to contrast the phenomena.
- Therefore, modelling the diffusion of criminal (or simply unlawful) behaviour in urban areas can be a tool that administrations and police authorities can use in order to choose optimal strategies to combat crime.
- And this is particularly important in a horizon of budget cuts that impose the best use of the existing resources, optimization of strategies, logistics etc.
- Of course “for complex phenomena as criminality (in its various guises), the goal is not to represent the whole reality, let alone generate precise predictions.
- The enhanced understanding of “stylized facts” that characterise a system of interest by isolating elements of a theoretical model can shed new light on the subject and contribute to new insights into the more complex global picture.
- With the aid of such models, one can then investigate the various effects implied by factors such as the severity of punishment, duration of imprisonment, different deterrence strategies, or the allocation of limited crime reduction resources in the most efficient way” .
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Q1. What have the authors contributed in "The role of mathematical modelling in modern criminology.comment on “statistical physics of crime: a review” by m.r. d'orsogna and m. perc" ?
Starting from the seminal paper by G. Becker [ 3 ], the study of crime and criminality from the point of view of economics has been developed in several directions. Thus, the paper [ 6 ] appears to be extremely timely and useful. Therefore, modelling the diffusion of criminal ( or simply unlawful ) behaviour in urban areas can be a tool that administrations and police authorities can use in order to choose optimal strategies to combat crime. This description can in turn suggest how to contrast the phenomena.