Davies, T;
(2020)
Mathematics.
In:
Routledge Handbook of Crime Science.
(pp. 117-130).
Routledge: London, UK.
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Abstract
The inherent complexity of criminal behaviour means that it is not a topic to which the field of mathematics has traditionally been thought to apply. In recent years, however, the study of crime has mirrored that of several other social phenomena in attracting increased attention from within the mathematical community. As well as being facilitated by a dramatic revolution in data availability, this has largely been driven by the growth of mathematical tools designed to confront the challenges presented by such systems; an approach encapsulated by the term ‘complexity science’. This chapter outlines the ways in which mathematical approaches can contribute to the understanding of crime, with particular emphasis on the role of modelling in offering insight into the mechanisms underlying criminal phenomena. A number of mathematical approaches are reviewed in this context, including dynamical systems theory, network science and game theory. Following this, previous research of this type on a number of criminological topics is reviewed, before the chapter concludes by considering the outlook for work in this area and some key issues which remain to be resolved.
Type: | Book chapter |
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Title: | Mathematics |
ISBN: | 0367580411 |
ISBN-13: | 9780367580414 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.4324/9780203431405 |
Publisher version: | https://doi.org/10.4324/9780203431405 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10117102 |
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