Summary
GANGNET is a project motivated by two intertwined emergencies of contemporary EU urban landscape: (1) the emergence of stable co-offending groups (2) the rapid propagation of inter-group violence (e.g. “knife epidemics” in UK). The key of my research is the use of innovative techniques from network theory and computational methods to inform regulators with better gang-crime risk prediction and more effective containment strategies.
Relational networks are recognized as drivers of individual’s criminal activity: criminal groups are both organizations as well as social environments. For members of co-offending groups, interaction is both at group-level and at inter-group level. At the current state of art, little is known about the channel by which inter-personal links affect inter-group dynamics and trigger systemic phenomena. Therefore, it is unclear what individual-level factors EU policy-makers should monitor to control systemic urgencies such as the outbreak of group violence epidemics or formation of criminal alliances. Regulators contain crime via offender or groups-focused devices. However, as offenders and groups act within endogenous networks, unintended consequences such as increased inter-gang instability and violence can emerge as the result of spurious containment attempts.
The project uncovers the theoretical and empirical structure of gang dynamics by adopting a network perspective. The goal is to understand how individuals act upon the influence of a stratified social network and to what extent isolated behaviour from single individuals can trigger inter-group system-wide dynamics. The project is developed along three directions aiming to understand how: (1) interaction between incentive-driven offenders determine group-level activities (2) group-level activities can lead to systemically relevant phenomena that unfold through relational networks (3) develop synthetic metrics to measure effectiveness of individual or group based containment policies
Relational networks are recognized as drivers of individual’s criminal activity: criminal groups are both organizations as well as social environments. For members of co-offending groups, interaction is both at group-level and at inter-group level. At the current state of art, little is known about the channel by which inter-personal links affect inter-group dynamics and trigger systemic phenomena. Therefore, it is unclear what individual-level factors EU policy-makers should monitor to control systemic urgencies such as the outbreak of group violence epidemics or formation of criminal alliances. Regulators contain crime via offender or groups-focused devices. However, as offenders and groups act within endogenous networks, unintended consequences such as increased inter-gang instability and violence can emerge as the result of spurious containment attempts.
The project uncovers the theoretical and empirical structure of gang dynamics by adopting a network perspective. The goal is to understand how individuals act upon the influence of a stratified social network and to what extent isolated behaviour from single individuals can trigger inter-group system-wide dynamics. The project is developed along three directions aiming to understand how: (1) interaction between incentive-driven offenders determine group-level activities (2) group-level activities can lead to systemically relevant phenomena that unfold through relational networks (3) develop synthetic metrics to measure effectiveness of individual or group based containment policies
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101022681 |
| Start date: | 01-10-2021 |
| End date: | 28-02-2024 |
| Total budget - Public funding: | 212 933,76 Euro - 212 933,00 Euro |
Cordis data
Original description
GANGNET is a project motivated by two intertwined emergencies of contemporary EU urban landscape: (1) the emergence of stable co-offending groups (2) the rapid propagation of inter-group violence (e.g. “knife epidemics” in UK). The key of my research is the use of innovative techniques from network theory and computational methods to inform regulators with better gang-crime risk prediction and more effective containment strategies.Relational networks are recognized as drivers of individual’s criminal activity: criminal groups are both organizations as well as social environments. For members of co-offending groups, interaction is both at group-level and at inter-group level. At the current state of art, little is known about the channel by which inter-personal links affect inter-group dynamics and trigger systemic phenomena. Therefore, it is unclear what individual-level factors EU policy-makers should monitor to control systemic urgencies such as the outbreak of group violence epidemics or formation of criminal alliances. Regulators contain crime via offender or groups-focused devices. However, as offenders and groups act within endogenous networks, unintended consequences such as increased inter-gang instability and violence can emerge as the result of spurious containment attempts.
The project uncovers the theoretical and empirical structure of gang dynamics by adopting a network perspective. The goal is to understand how individuals act upon the influence of a stratified social network and to what extent isolated behaviour from single individuals can trigger inter-group system-wide dynamics. The project is developed along three directions aiming to understand how: (1) interaction between incentive-driven offenders determine group-level activities (2) group-level activities can lead to systemically relevant phenomena that unfold through relational networks (3) develop synthetic metrics to measure effectiveness of individual or group based containment policies
Status
CLOSEDCall topic
MSCA-IF-2020Update Date
28-04-2024
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