GCR: Collaborative Research: Disentangling Environmental Change and Social Factors as Drivers of Migration

这个日益趋同研究项目的目标是对大规模人类迁徙进行全面分析，并提高我们预测它们的能力。大规模移民所带来的挑战促使我们研究区域和国际移民流动在未来将如何变化，以及这些流动对不断变化的社会和环境条件有多敏感。研究小组的目标是实现地理学、经济学、政治学、环境科学和作物科学的融合，以更好地了解移民的原因并预测未来的大规模移民。这种理解将使社会能够更好地预测、适应和管理这种移民，从而最大限度地提高来源国和目的地国的人类福祉。在拟议的工作中，一组研究人员是与移民相关的多个学科领域的专家，他们将共同努力，超越学科界限，开发一种共同的语言和方法，以理解、分析、建模和预测社会、经济和环境背景下的移民及其对粮食生产、安全和家庭生计的影响。该团队将参与有意的融合活动，所有团队成员将与利益相关者一起使用社会、经济和环境数据和模型来分析移民问题的复杂性。这一分析将转化为预测模型，并根据历史数据进行校准和验证。建模工作将把气候、作物和全球食品贸易模型与家庭生计模型结合起来。这将推动基于代理的移民决策模型，该模型考虑了对机会和风险、移民和家庭网络、资源和标准经济效用最大化模型的看法。集成的建模将根据需要根据涉众社区的输入进行开发和修改。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The objectives of this Growing Convergence Research project are to develop a comprehensive analysis of large-scale human migrations and improve our ability to predict them. The challenges that drive and are created by large-scale migrations motivate this research into how regional and international migrant flows will change in the future and how sensitive these flows are to changing social and environmental conditions. The research team aims to achieve convergence across geography, economics, political science, environmental science, and crop sciences to better understand the causes of migration and predict future mass-migrations. Such understanding will allow society to better anticipate, adapt to, and manage such migrations to maximize human wellbeing in both source and destination countries.In the proposed work, a team of researchers who are individually experts in the multiple subject areas relevant to migration will work together to transcend their disciplinary boundaries and develop a common language and methodology for understanding, analyzing, modeling and predicting migration within social, economic, and environmental contexts and their impacts on food production, security, and household livelihoods. The team will engage in intentional convergence activities where all team members together with stakeholders will work with social, economic and environmental data and models to analyze the complexity of the migration issues. This analysis will be translated into predictive models that will be calibrated and verified against historical data. The modeling effort will couple climate, crop, and global food trade models with models of household livelihoods. These will drive agent-based models of migration decisions that account for perceptions of opportunity and risk, migrant and family networks, resources, and standard economic utility maximizing models. The integrated modeling will be developed and modified as needed with input from the stakeholder community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.