Towards a ‘smart’ cost–benefit tool: using machine learning to predict the costs of criminal justice policy interventions

Towards a ‘smart’ cost–benefit tool: using machine learning to predict the costs of criminal justice policy interventions

Abstract Background The Manning Cost–Benefit Tool (MCBT) was developed to assist criminal justice policymakers, policing organisations and crime prevention practitioners to assess the benefits of different interventions for reducing crime and to select those strategies that represent the greatest ec...

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Bibliographic Details
Main Authors: Matthew Manning, Gabriel T. W. Wong, Timothy Graham, Thilina Ranbaduge, Peter Christen, Kerry Taylor, Richard Wortley, Toni Makkai, Pierre Skorich
Format: Article
Language:English
Published: BMC 2018-10-01
Series:Crime Science
Subjects:
Cost–benefit analysis
Machine learning
Cost–benefit tools
Data science
Online Access:http://link.springer.com/article/10.1186/s40163-018-0086-4

Internet

http://link.springer.com/article/10.1186/s40163-018-0086-4

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