Title |
The Relative Ineffectiveness of Criminal Network Disruption
|
---|---|
Published in |
Scientific Reports, February 2014
|
DOI | 10.1038/srep04238 |
Pubmed ID | |
Authors |
Paul A. C. Duijn, Victor Kashirin, Peter M. A. Sloot |
Abstract |
Researchers, policymakers and law enforcement agencies across the globe struggle to find effective strategies to control criminal networks. The effectiveness of disruption strategies is known to depend on both network topology and network resilience. However, as these criminal networks operate in secrecy, data-driven knowledge concerning the effectiveness of different criminal network disruption strategies is very limited. By combining computational modeling and social network analysis with unique criminal network intelligence data from the Dutch Police, we discovered, in contrast to common belief, that criminal networks might even become 'stronger', after targeted attacks. On the other hand increased efficiency within criminal networks decreases its internal security, thus offering opportunities for law enforcement agencies to target these networks more deliberately. Our results emphasize the importance of criminal network interventions at an early stage, before the network gets a chance to (re-)organize to maximum resilience. In the end disruption strategies force criminal networks to become more exposed, which causes successful network disruption to become a long-term effort. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Netherlands | 6 | 16% |
Canada | 4 | 11% |
United States | 4 | 11% |
United Kingdom | 3 | 8% |
Italy | 2 | 5% |
India | 1 | 3% |
Hungary | 1 | 3% |
Denmark | 1 | 3% |
Unknown | 15 | 41% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 29 | 78% |
Scientists | 6 | 16% |
Science communicators (journalists, bloggers, editors) | 2 | 5% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | <1% |
Portugal | 1 | <1% |
Netherlands | 1 | <1% |
Brazil | 1 | <1% |
Italy | 1 | <1% |
Russia | 1 | <1% |
United Kingdom | 1 | <1% |
Unknown | 254 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 54 | 21% |
Student > Master | 37 | 14% |
Student > Bachelor | 33 | 13% |
Researcher | 29 | 11% |
Student > Doctoral Student | 16 | 6% |
Other | 40 | 15% |
Unknown | 53 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Social Sciences | 68 | 26% |
Computer Science | 26 | 10% |
Psychology | 17 | 6% |
Physics and Astronomy | 12 | 5% |
Agricultural and Biological Sciences | 11 | 4% |
Other | 61 | 23% |
Unknown | 67 | 26% |