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Simulating protection rackets: a case study of the Sicilian Mafia

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Abstract

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.

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Notes

  1. The model is named Palermo Scenario because most of the empirical data used to develop it was collected in the area of Palermo. Despite its name, it is worth noting that the model is flexible enough to represent the dynamics behind other protection racketeering groups.

  2. Another part of this is likely down to a selection effect in that those groups which are not entrenched in their milieu do not survive.

  3. There is not an exact number of individuals affiliated to the Sicilian Mafia, but according to some official sources [7, p. 16], the number should be considerably lower, perhaps as low as 2000, compared to the 3000 of the mid-1990s.

  4. The provinces of Palermo, Trapani, and Agrigento are particularly involved [7, pp. 28–70].

  5. During the early 1980s, the second mafia war took place. It was driven by the family from Corleone against other families from Palermo.

  6. Pentiti designate former members of criminal organisations that, in most cases following their arrest, decide to collaborate with the judicial system to help investigations.

  7. http://www.libera.it.

  8. http://www.addiopizzo.org.

  9. These discussions were carried out under the FP7 GLODERS project (http://www.gloders.eu, Global Dynamics of Extortion Racket Systems).

  10. These sources are judicial documents, confiscated mafia documents, academic studies, literature, and other sources, such as newspapers and television interviews.

  11. Given the lack of an English word that means extortion money paid to mafia, we employ the Italian term pizzo that has this meaning.

  12. The Fondo di Solidarietà contains resources that depend on a politically determined component and a component derived from the resources confiscated of captured mafiosi.

  13. This model is not aimed at analysing the internal structure of the mafia and other important dynamics, such as fights between different mafia groups for the domain on the territory. For an analysis on this aspect of the mafia, please refer to [50].

  14. The main features of the Intermediary Organisation actor have been extracted from discussions and interviews with Daniele Marannano, one of the leaders of Addiopizzo.

  15. Immergence refers to a gradual process in which the emergent effects determine new mental mechanisms in the agents involved, who are not necessarily aware of the effects produced [14].

  16. The implementation of the EMIL-A used in this work can be downloaded at https://www.github.com/gnardin/emilia.

  17. In previous work [5] we distinguish between two enforcement mechanisms, punishment and sanction, which have different capacities to convey normative information. Punishment works only by imposing a cost on the wrongdoer, reducing his or her material payoffs. In addition to inflicting a cost, sanction also communicates that the sanctioned behaviour is not approved of because it violated a norm. Sanctions convey a great deal of norm-relevant information that has the effect of making norms explicit and increasing their salience.

  18. Available for download at https://www.github.com/gnardin/gloderss.

  19. The numbers of agents of each type are arbitrary. However, we assume that 5 Entrepreneurs per Mafioso is a reasonable number to be handled by an individual. Moreover, the number of Police Officers range from 5 to 20, meaning that in an extreme case there is the same number of Police Officers as Mafiosi.

  20. All statistical significance tests shown in this paper are performed using the Wilcoxon Rank Sum Test with \(\alpha = 0.05\) [35, pp. 68–75] We chose this test due to the fact that our data cannot be assumed normally distributed under the Shapiro-Wilk test [64].

  21. We are aware that several other factors may have influenced this change; however, here we model only the normative aspect.

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Acknowledgments

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. This work reflects the views solely of its authors. The European Commission is not liable for any use that may be made of the information in the work. We also acknowledge the invaluable comments and suggestions of the anonymous reviewers that helped improving the paper in several aspects.

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Authors and Affiliations

  1. Institute of Cognitive Sciences and Technologies, CNR, Via Palestro 32, 00185, Rome, Italy

    Luis G. Nardin, Giulia Andrighetto, Rosaria Conte & Áron Székely

  2. European University Institute, 50014, Fiesole, Italy

    Giulia Andrighetto

  3. Universität Koblenz-Landau, Universitätsstraße 1, 56070, Koblenz, Germany

    Ulf Lotzmann, Martin Neumann & Klaus G. Troitzsch

  4. Centre for Research in Social Simulation, University of Surrey, Guildford, GU2 7XH, UK

    David Anzola & Corinna Elsenbroich

  5. Department of Law, Society and Sport, University of Palermo, Palermo, Italy

    Valentina Punzo

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  1. Luis G. Nardin
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Nardin, L.G., Andrighetto, G., Conte, R. et al. Simulating protection rackets: a case study of the Sicilian Mafia. Auton Agent Multi-Agent Syst 30, 1117–1147 (2016). https://doi.org/10.1007/s10458-016-9330-z

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