Causal Models Applied to the Patterns of Human Migration due to Climate Change
Abstract: The impacts of mass migration, such as crisis induced by climate change, extend beyond environmental concerns and can greatly affect social infrastructure and public services, such as education, healthcare, and security. These crises exacerbate certain elements like cultural barriers, and discrimination by amplifying the challenges faced by these affected communities. This paper proposes an innovative approach to address migration crises in the context of crisis management through a combination of modeling and imbalance assessment tools. By employing deep learning for forecasting and integrating causal reasoning via Bayesian networks, this methodology enables the evaluation of imbalances and risks in the socio-technological landscape, providing crucial insights for informed decision-making. Through this framework, critical systems can be analyzed to understand how fluctuations in migration levels may impact them, facilitating effective crisis governance strategies.
- R. Andersson, “Europe’s failed ‘fight’ against irregular migration: ethnographic notes on a counterproductive industry,” Journal of ethnic and migration studies, vol. 42, no. 7, pp. 1055–1075, 2016.
- P. Lu, “How systemic imbalance shapes migration patterns: Evidence from china (1949–2012),” Chinese Sociological Dialogue, vol. 2, no. 1-2, pp. 3–17, 2017.
- J. Estevens, “Migration crisis in the eu: developing a framework for analysis of national security and defence strategies,” Comparative migration studies, vol. 6, no. 1, p. 28, 2018.
- B. Lillywhite and G. Wolbring, “Emergency and disaster management, preparedness, and planning (edmpp) and the ‘social’: A scoping review,” Sustainability, vol. 14, no. 20, p. 13519, 2022.
- M. McAuliffe, J. Blower, and A. Beduschi, “Digitalization and artificial intelligence in migration and mobility: Transnational implications of the covid-19 pandemic,” Societies, vol. 11, no. 4, p. 135, 2021.
- M. Cheesman, “Self-sovereignty for refugees? the contested horizons of digital identity,” Geopolitics, vol. 27, no. 1, pp. 134–159, 2022.
- A. Corte-Real, T. Nunes, and P. R. da Cunha, “Blockchain technology in migrant and refugee health: A scoping review,” Journal of Global Health, vol. 12, 2022.
- S. N. Yanushkevich, K. W. Sundberg, N. W. Twyman, R. M. Guest, and V. P. Shmerko, “Cognitive checkpoint: Emerging technologies for biometric-enabled watchlist screening,” Computers & Security, vol. 85, pp. 372–385, 2019.
- J. Bi, S. Li, H. Yuan, and M. Zhou, “Integrated deep learning method for workload and resource prediction in cloud systems,” Neurocomputing, vol. 424, pp. 35–48, 2021.
- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.
- H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, and W. Zhang, “Informer: Beyond efficient transformer for long sequence time-series forecasting,” in Proceedings of the AAAI conference on artificial intelligence, vol. 35, no. 12, 2021, pp. 11 106–11 115.
- H. Wu, J. Xu, J. Wang, and M. Long, “Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,” Advances in Neural Information Processing Systems, vol. 34, pp. 22 419–22 430, 2021.
- R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast accuracy,” International journal of forecasting, vol. 22, no. 4, pp. 679–688, 2006.
- S. Makridakis, “Accuracy measures: theoretical and practical concerns,” International journal of forecasting, vol. 9, no. 4, pp. 527–529, 1993.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
