An agent based model of camorra: Comparing punishment and norm-based policies in contrasting illegal activities

In this chapter, we will discuss the need of Agent Based Modelling (ABM) to study the dynamics of a specific type of illegal system, i.e., Extortion Racket Systems, which appear to be highly prosperous and to behave as a dynamic system, spreading wide and fast in current Western societies. This work arises from two traditions of study: one is related to the deviance issue and the spread of organized crime, the other on the socio-cognitive study of norms. The strength of the realized simulation model is based on two factors: the reference to a previous empirical grounded model that it's complemented with the use of cognitively rich agents. More specifically, we have implemented a case study, resembling as much as possible the Camorra phenomenon in Campania, aimed to test the relative and combined effect of punishment and norms in contrasting the spreading of Extortion Racket Systems. Results show that to be effective policies based only on punishment should be very severe. Nevertheless, when punishment is combined with norms, their effect in reducing the number of racket affiliates is not only stronger but also more stable over time with respect to punishment alone. These results enlighten the limits of the anti-crime strategies based merely on the use of punishment, and show the advantages of a multi-faceted policy that incorporates traditional, i.e., economic, and non-traditional, i.e., normative, factors.

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Contributo in volume
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Sonzogni B.
Cecconi F.
Andrighetto G.
Conte R.
The Cognitive foundations of group attitudes and social interaction, edited by Herzig, Andreas; Lorini, Emiliano, pp. 191–202, 2015
info:cnr-pdr/source/autori:Sonzogni B.; Cecconi F.; Andrighetto G.; Conte R./titolo:An agent based model of camorra: Comparing punishment and norm-based policies in contrasting illegal activities/titolo_volume:The Cognitive foundations of group attitudes
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Giulia Andrighetto's picture
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Federico Cecconi's picture
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