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ObjectivesWe test the effects of four policy scenarios on recruitment into organized crime. The policy scenarios target (i) organized crime leaders and (ii) facilitators for imprisonment, (iii) provide educational and welfare support to children and their mothers while separating them from organized-crime fathers, and (iv) increase educational and social support to at-risk schoolchildren.MethodsWe developed a novel agent-based model drawing on theories of peer effects (differential association, social learning), social embeddedness of organized crime, and the general theory of crime. Agents are simultaneously embedded in multiple social networks (household, kinship, school, work, friends, and co-offending) and possess heterogeneous individual attributes. Relational and individual attributes determine the probability of offending. Co-offending with organized crime members determines recruitment into the criminal group. All the main parameters are calibrated on data from Palermo or Sicily (Italy). We test the effect of the four policy scenarios against a baseline no-intervention scenario on the number of newly recruited and total organized crime members using Generalized Estimating Equations models.ResultsThe simulations generate realistic outcomes, with relatively stable organized crime membership and crime rates. All simulated policy interventions reduce the total number of members, whereas all but primary socialization reduce newly recruited members. The intensity of the effects, however, varies across dependent variables and models.ConclusionsAgent-based models effectively enable to develop theoretically driven and empirically calibrated simulations of organized crime. The simulations can fill the gaps in evaluation research in the field of organized crime and allow us to test different policies in different environmental contexts.

First Online: 01 September 2016 Computer simulation has recently been recognised by criminologists as a useful tool for bridging the gap between theoretical and empirical analyses of organised crime and for supplementing their weaknesses. GLODERS-S is an innovative and configurable agent-based simulator specialised in reproducing the dynamics of a specific type of criminal organisations: protection racketeering groups. The simulator adopts an event-based approach that provides a more realistic operation of the agents, which integrated with its configurability provides policy-makers with a highly flexible platform for analysing multiple scenarios and assessing policies to counter organised crime. In this paper, we describe the principles of the simulator design, its features and limitations, and possible applications.

Protection racketeering groups are powerful, deeply entrenched in multiple societies across the globe, and they harm the societies and economies in which they operate in multiple ways. These reasons make their dynamics important to understand and an objective of both scientific and application-oriented interest. Legal and social norm-based approaches arguably play significant roles in influencing protection racket dynamics. We propose an agent-based simulation model, the Palermo Scenario, to enrich our understanding of these influences and to test the effect of different policies on protection racket dynamics. Our model integrates the legal and the social norm-based approaches and uses a complex normative agent architecture that enables the analysis of both agents' behaviours and mental normative representations driving behaviour. We demonstrate the usefulness of the model and the benefits of using this complex normative architecture through a case study of the Sicilian Mafia. This work was partially supported by the FP7-ICT Science of Global Systems programme of the European Commission through Project GLODERS (http://www.gloders.eu, Global Dynamics of Extortion Racket Systems) under Grant agreement no.: 315874.