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An agent-based framework to study forced migration: A case study of Ukraine.

Zakaria Mehrab1,2, Logan Stundal1,3, Srinivasan Venkatramanan1

  • 1Biocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA 22904, USA.

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|March 20, 2024
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Summary
This summary is machine-generated.

This study presents an agent-based framework to predict migrant outflow from conflict zones, aiding humanitarian aid and policy decisions. It forecasts future migration based on conflict scenarios, reducing uncertainty for resource allocation.

Keywords:
Ukraineagent-based modelingdigital twinforced migrationpolicy analysissocial theories

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Area of Science:

  • Computational Social Science
  • Forced Migration Studies
  • Conflict Analysis

Background:

  • The Russian aggression in Ukraine has caused a significant refugee crisis, with over eight million people migrating.
  • Understanding forced migration dynamics is critical for effective policy-making and humanitarian aid delivery.
  • Current research on migration patterns is limited by delayed observational data.

Purpose of the Study:

  • To evaluate a data-driven agent-based framework for predicting migrant outflow during the Ukraine war.
  • To demonstrate the framework's utility in addressing policy questions using refugee demographics.
  • To incorporate conflict forecasts for predicting future migration flows.

Main Methods:

  • Development of a data-driven agent-based framework incorporating social and behavioral theories.
  • Simulation of migrant outflow in response to conflict events during the initial phase of the Ukraine war.
  • Integration of conflict forecast scenarios to predict future migration.

Main Results:

  • The framework effectively predicts migrant outflow resulting from conflict events.
  • Demonstrated policy use cases leveraging refugee demographic data.
  • Successful incorporation of conflict scenarios for future migration flow prediction.

Conclusions:

  • The agent-based framework offers a proactive tool for understanding and predicting conflict-induced migration.
  • The model aids policymakers by reducing uncertainty and improving humanitarian resource allocation.
  • This approach enhances preparedness and response to large-scale displacement crises.