Overview of the forced migration case study
Forced migration continues to be a pressing issue around the world, highlighted by the crises in Syria, Ukraine, and Sudan. This case study was made by researchers from George Mason University and the University at Buffalo for the Humanitarian Information Unit—State Department. Together, they introduced a different way to understand forced migration using a system dynamics model.
Sudan humanitarian crisis map, June 2023 (click to enlarge)
Unlike most studies that might use simple, static models, system dynamics helps to see how different factors like conflict or government stability connect and influence migration patterns.
This approach is particularly useful as it can include a variety of real-time data from open sources and social media. This helps to get a clearer picture of what's happening on the ground.
The goal of this forced migration case study was to provide new insights into this issue. The researchers wanted to create a simulation model to help organizations and governments better prepare for and respond to migration challenges using forecasting techniques.
Problems in forecasting migration and refugee flows
One major challenge in migration forecasting is the slow pace at which official data becomes available. Often, by the time the researchers get the data, it’s already outdated. Another problem is that traditional forced migration studies don’t incorporate the rapid changes in factors like conflict intensity, economic conditions, or environmental crises that drive people to move.
Additionally, existing models rarely manage to use different types of data together, which means they miss out on understanding how these factors work together to push people to migrate. This lack of integration leads to a fragmented view of migration and refugee flows. It becomes difficult to predict how many people will move, where they will go, or what they need.
Solution: system dynamics simulation model
The researchers used the system dynamics approach to forecast the migration of refugees by building a simulation model using AnyLogic software. This model reflected the complex and often interconnected nature of the factors that drive forced migration.
For the simulation model on forced migration, researchers used a mix of real-time open-source and authoritative data. Key parameter values were:
- Regime legitimacy
- Human rights violations
- Conflict
- Regime-force violence
- Socio-political mobilization
- Intervening Opportunities
- Contact rate
Researchers could see how these factors interact and influence forced migration. They fed the model with a variety of data sources, from United Nations High Commissioner for Refugees reports to real-time social media data.
Above, you can see a system dynamics simulation model posted on the AnyLogic Cloud platform. You can try and test out the model yourself.
The model shows how changes in one area, like increasing conflict or worsening economic conditions, affect how many people decide to move. By tweaking these factors in the model, researchers could simulate what might happen under different scenarios. The goal was for governments and organizations to predict refugee flows and respond to migration trends effectively.
Results of the case study on forced migration
By creating the system dynamics model, researchers gained clear insights into migration. The model matched well with real-world data from 2012 to 2018, which proved it could accurately forecast migration trends.
Refugee routes in migration forecasting
Here’s what researchers found in this case study on forecasting forced migration:
- More conflict leads directly to more people moving, both within Sudan and to other countries.
- Economic problems and environmental issues also push people to leave, especially when combined with political instability.
- Changes in government stability can speed up or slow down the migration of refugees, depending on whether the change makes things better or worse.
These findings from the case study on forced migration can help governments and organizations better plan and predict the future migration of refugees in developing countries. The case study showed the potential future migration patterns of refugees. This way, the entities can allocate resources more effectively and ensure aid reaches where it's most needed.
The case study was presented by Troy Curry from George Mason University at the AnyLogic Conference 2023.
The slides are available as a PDF.
