The situation in Ukraine has resulted in massive forced displacement, with nearly a quarter of its pre-war population having fled their homes, including approximately 5.6 million refugees and over 3.7 million internally displaced persons. While periodic surveys of refugee intentions have been conducted by UNHCR and Ipsos SA, revealing a gradual decline in return intentions due to ongoing conflict and integration into host communities, these surveys lack the granular information crucial for effective recovery planning within Ukraine.

To address this data gap, UNHCR, in partnership with the Department of Computer Science at Brunel University of London, have developed an new agent-based modelling (ABM) prototype.

What is agent-based modelling (ABM)? ABM is a powerful tool designed to simulate complex systems by focusing on individual-level dynamics. Unlike traditional models that rely on aggregate data, ABM aims to capture the heterogeneity and complexity within a system, projecting how macro-level phenomena emerge from varied micro-level behaviours.

• Inform reconstruction prioritisation: Helps identify oblasts for early investments in crucial sectors like housing, health, education, and utilities, based on projected returnee numbers and their specific socio-demographic needs.

• Test the impact of potential policies or investments: Allows policymakers to simulate and understand the outcomes of different policy interventions and investments on future return intentions, including efforts to foster social cohesion.

• Contribute to socio-economic reintegration: Aids in designing tailored programmes for returnees, such as vocational training, psychosocial support, job-matching, care services, employability schemes, and housing solutions.

• Promote effective inclusion in host countries: Improves the design of long-term inclusion policies for refugees unlikely to return in the short term, enabling them to contribute to host societies and develop their human capital.

• Contribute to regional coordination and coherence: Helps inform harmonised policies among EU Member States that support Ukrainians' ability to develop human capital and enable voluntary and sustainable returns, while mitigating fragmentation.

• Inform the ongoing transition from emergency response to durable solutions: Guides a shift from emergency humanitarian support to programmes that foster sustainable inclusion and reintegration, while maintaining readiness for potential renewed outflows.

• Enhance capacities for modelling return across refugee situations elsewhere: The model's parameters can be adjusted for a variety of other refugee return situations globally, contingent on available data to inform it.

SEAVEA participants directly involved in this Use Case:

• Derek Groen, Diana Suleimenova, Yani Xue, Laura Harbach, Taulant Matarove, Farzeen Nadeem from Brunel University of London, UK.

• Yehor Yudin from Bangor University, UK.