Flee 3: Flexible agent-based simulation for forced migration

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Maziar Ghorbani , Diana Suleimenova , Alireza Jahani , Arindam Saha , Yani Xue , Kate Mintram , Anastasia Anagnostou , Auke Tas , William Low , Simon J.E. Taylor , Derek Groen
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引用次数: 0

Abstract

Forced migration is a major humanitarian challenge today, with over 100 million people forcibly displaced due to conflicts, violence and other adverse events. The accurate forecasting of migration patterns helps humanitarian organisations to plan an effective humanitarian response in times of crisis, or to estimate the impact of possible conflict and/or intervention scenarios. While existing models are capable of providing such forecasts, they are strongly geared towards forecasting headline arrival numbers and lack the flexibility to explore migration patterns for specific groups, such as children or persons of a specific ethnicity or religion. Within this paper we present Flee 3, an agent-based simulation tool that aims to deliver migration forecasts in a more detailed, flexible and reconfigurable manner. The tool introduces adaptable rules for agent movement and creation, along with a more refined model that flexibly supports factors like food security, ethnicity, religion, gender and/or age. These improvements help broaden the applicability of the code, enabling us to begin building models for internal displacement and non-conflict-driven migration. We validate Flee 3 by applying it to ten historical conflicts in Asia and Africa and comparing our results with UNHCR refugee data. Our validation results show that the code achieves a validation error (averaged relative difference) of less than 0.6 in all cases, i.e. correctly forecasting over 70% of refugee arrivals, which is superior to its predecessor in all but one case. In addition, by exploiting the parallelised simulation code, we are able to simulate migration from a large scale conflict (Ukraine 2022) in less than an hour and with 80% parallel efficiency using 512 cores per run. To showcase the relevance of Flee to practitioners, we present two use cases: one involving an international migration research project and one involving an international NGO. Flee 3 is available at https://github.com/djgroen/flee/releases/tag/v3.1 and documented on https://flee.readthedocs.io.

逃离 3:基于代理的灵活模拟强迫迁移
由于冲突、暴力和其他不利事件,1 亿多人被迫流离失所。准确预测迁徙模式有助于人道主义组织在危机时刻规划有效的人道主义应对措施,或估计可能发生的冲突和/或干预方案的影响。虽然现有模型能够提供此类预测,但它们主要针对预测抵达人数,缺乏探索特定群体(如儿童或特定种族或宗教人群)迁移模式的灵活性。在本文中,我们介绍了基于代理的模拟工具 Flee 3,该工具旨在以更加详细、灵活和可重新配置的方式提供移民预测。该工具引入了可调整的代理移动和创建规则,以及更精细的模型,可灵活支持粮食安全、种族、宗教、性别和/或年龄等因素。这些改进有助于扩大代码的适用性,使我们能够开始建立境内流离失所和非冲突驱动的迁移模型。我们将 Flee 3 应用于亚洲和非洲的十次历史冲突,并将结果与联合国难民署的难民数据进行比较,从而验证了 Flee 3 的有效性。我们的验证结果表明,代码在所有情况下的验证误差(平均相对差值)都小于 0.6,即正确预测了 70% 以上的难民抵达人数,除一种情况外,在其他所有情况下都优于其前身。此外,通过利用并行化模拟代码,我们能够在不到一小时的时间内模拟大规模冲突(2022 年的乌克兰)造成的移民,每次运行使用 512 个内核,并行效率高达 80%。为了展示 Flee 与实际工作者的相关性,我们介绍了两个使用案例:一个涉及国际移民研究项目,另一个涉及国际非政府组织。Flee 3 可在 https://github.com/djgroen/flee/releases/tag/v3.1 上获取,并在 https://flee.readthedocs.io 上记录。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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