Objectives

We 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.

Methods

We 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.

Results

The 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.

Conclusions

Agent-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.

中文翻译：

---

有组织犯罪的招募：基于代理的方法测试不同政策的影响

目标

我们测试了四种政策方案对招募有组织犯罪的影响。政策方案的目标是（i）有组织犯罪领导人和（ii）监禁人员，（iii）为儿童及其母亲提供教育和福利支持，同时将他们与有组织犯罪的父亲分开，以及（iv）增加对儿童及其母亲的教育和社会支持高危学童。

方法

我们基于同伴效应（差异联想，社会学习），有组织犯罪的社会根植性和犯罪的一般理论，开发了一种基于主体的新型模型。代理同时嵌入到多个社交网络（家庭，亲戚，学校，工作，朋友和共同犯罪）中，并具有不同的个人属性。关系和个人属性决定了犯罪的可能性。与有组织犯罪成员的共同犯罪决定将其招募入犯罪集团。所有主要参数均根据来自巴勒莫或西西里岛（意大利）的数据进行了校准。我们使用广义估计方程模型，针对基线的不干预情景，测试了四个政策情景对新招募和有组织犯罪成员总数的影响。

结果

模拟产生了现实的结果，组织犯罪成员和犯罪率相对稳定。所有模拟的政策干预措施都会减少成员总数，而基本社会化之外的所有措施都会减少新招募的成员。但是，影响的强度因因变量和模型而异。

结论

基于代理的模型可以有效地开发有组织犯罪的理论驱动和经验校准的模拟。这些模拟可以填补有组织犯罪领域评估研究中的空白，并允许我们在不同的环境环境中测试不同的政策。