Social Dimensions of Organised Crime: Modelling the Dynamics of Extortion Rackets
Elsenbroich, C, Anzola, D and Gilbert, GN (2016) Social Dimensions of Organised Crime: Modelling the Dynamics of Extortion Rackets Computational Social Sciences . Springer International Publishing AG. ISBN 978-3-319-45167-1
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This book presents a multi-disciplinary investigation into extortion rackets with a particular focus on the structures of criminal organisations and their collapse, societal processes in which extortion rackets strive and fail and the impacts of bottom-up and top-down ways of fighting extortion racketeering. Through integrating a range of disciplines and methods the book provides an extensive case study of empirically based computational social science. It is based on a wealth of qualitative data regarding multiple extortion rackets, such as the Sicilian Mafia, an international money laundering organisation and a predatory extortion case in Germany. Computational methods are used for data analysis, to help in operationalising data for use in agent-based models and to explore structures and dynamics of extortion racketeering through simulations. In addition to textual data sources, stakeholders and experts are extensively involved, providing narratives for analysis and qualitative validation of models. The book presents a systematic application of computational social science methods to the substantive area of extortion racketeering. The reader will gain a deep understanding of extortion rackets, in particular their entrenchment in society and processes supporting and undermining extortion rackets. Also covered are computational social science methods, in particular computationally assisted text analysis and agent-based modelling, and the integration of empirical, theoretical and computational social science.
|Date :||October 2016|
|Copyright Disclaimer :||© 2016 Springer International Publishing Switzerland|
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|Depositing User :||Symplectic Elements|
|Date Deposited :||28 Feb 2017 13:43|
|Last Modified :||28 Feb 2017 13:43|
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