Spatial agent-based modelling of smuggling dynamics in the Niger-Libya corridor

基于空间主体的尼日尔-利比亚走廊走私动态建模

• 批准号: 2094920
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2094920
• 关键词: Spatial; agent; based; modelling; smuggling

Centuries-old smuggling networks in the Sahel have had devastating effects on the political economy of the region. Operating between the porous borders of Sahelian countries, these criminal networks have facilitated the flows of weapons, drugs, and humans. Recent EU-backed interventions targeting primarily Libya and Niger have reduced criminal operations, as seen by the significant fall in irregular migrant flows from West Africa to Europe in 2017. However, the adverse effects of smuggling interdictions in a region where the smuggling business is embedded in the social fabric have not been mitigated. Moreover, as the underlying conditions and drivers for the migrant crisis remain in place in source countries, criminal networks are unlikely to see demand for smuggling decrease. Combined with increased government-backed interdiction measures, this demand pushes smugglers to use more dangerous, unmonitored routes and adopt innovative tactics. The current interdiction measures will therefore most likely only be effective in the short term. Hence, there is a crucial need to empirically understand how to best tackle smuggling dynamics in the Sahel.This research will reconstruct and model the structure, activities and behaviours of migrant smuggling networks operating in the Niger-Libya corridor. Using spatial analysis and agent-based modelling, it will investigate how smuggling networks respond to counter-smuggling measures. It will especially examine changes in tactics and routes. An agent-based model is a class of computational models that simulates the actions and interactions of autonomous agents with a view of re-creating an artificial environment and predicting the appearance of complex phenomena. Given the scarcity in data, an agent-based model will allow for the testing of interventions and their effects on smuggling networks, providing a key empirical study both for the academic literature in the field of organised crime and for European and African policy-makers. The model will be calibrated with data collected in the field through interviews and surveys with smugglers and migrants, as well as satellite imagery and other secondary sources of data on migration.

萨赫勒地区数百年的走私网络对该地区的政治经济产生了毁灭性的影响。这些犯罪网络在萨赫勒国家漏洞百出的边界之间活动，为武器、毒品和人口的流动提供了便利。欧盟最近支持的主要针对利比亚和尼日尔的干预措施减少了犯罪行动，2017年从西非流向欧洲的非正常移民人数大幅下降就是明证。然而，在一个走私业务植根于社会结构的区域，截获走私的不利影响并未得到缓解。此外，由于移民危机的基本条件和驱动因素在来源国仍然存在，犯罪网络不太可能看到走私需求减少。再加上政府支持的更多拦截措施，这种需求促使走私者使用更危险、更不受监控的路线，并采取创新的战术。因此，目前的拦截措施很可能只在短期内有效。因此，迫切需要从经验上了解如何最好地解决萨赫勒地区的走私问题。这项研究将重建在尼日尔-利比亚走廊活动的移民走私网络的结构、活动和行为，并对其进行建模。它将利用空间分析和基于代理人的建模，调查走私网络如何对反走私措施作出反应。它将特别研究战术和路线的变化。基于智能体的模型是一类模拟自治智能体的行为和交互的计算模型，其目的是重新创建人工环境并预测复杂现象的出现。鉴于数据的稀缺性，基于代理人的模型将能够测试干预措施及其对走私网络的影响，为有组织犯罪领域的学术文献以及欧洲和非洲的政策制定者提供关键的实证研究。该模型将根据实地通过与走私者和移民的访谈和调查收集的数据以及卫星图像和其他二手移民数据来源进行校准。