Joined by leaders in the field of migration and displacement, David Leblang writes about forced migration in Ukraine using agent-based frameworks and models. “A Generalized Theory-Driven Agent-Based Framework” was published in conjunction with Association for the Advancement of Artificial
Intelligence and “Network Agency” was published in conjunction with the “International Foundation for Autonomous Agents and Multiagent Systems.”

A Generalizable Theory-Driven Agent-Based Framework to Study Conflict-Induced Forced Migration: Large-scale population displacements arising from conflict induced forced migration generate uncertainty and introduce several policy challenges. Addressing these concerns requires an interdisciplinary approach that integrates knowledge from both computational modeling and social sciences. We propose a generalized computational agent-based modeling framework grounded by Theory of Planned Behavior to model conflict-induced migration outflows within Ukraine during the start of that conflict in 2022. Existing migration modeling frameworks that attempt to address policy implications primarily focus on destination while leaving absent a generalized computational framework grounded by social theory focused on the conflict-induced region. We propose an agent-based framework utilizing a spatiotemporal gravity model and a Bi-threshold model over a Graph Dynamical System to update migration status of agents in conflict induced regions at fine temporal and spatial granularity. This approach significantly outperforms previous work when examining the case of Russian invasion in Ukraine. Policy implications of the proposed framework are demonstrated by modeling the migration behavior of Ukrainian civilians attempting to flee from regions encircled by Russian forces. We also showcase the generalizability of the model by simulating a past conflict in Burundi, an alternative conflict setting. Results demonstrate the utility of the framework for assessing conflict-induced migration in varied settings as well as identifying vulnerable civilian populations.

Network Agency – An Agent-based Model of Forced Migration from Ukraine: Individuals in social systems are embedded in collective decision making hierarchies, such as households, neighborhoods, communities, organizations, etc. The locus of agency in such systems is dispersed across the system, and can variously be viewed as individual, distributed, and shared agency. Here we propose a general notion of network agency that subsumes these descriptions and also allows for integrating related notions, such as peer influence. In our view, the social system can be seen as a multi-layer network, where each layer corresponds to different aggregations of the underlying units, representing different kinds of perception and decision-making. We illustrate this general framework with an agent-based model of the ongoing forced migration from Ukraine. In our model, individuals perceive hazards (conflict events), but decisions to migrate are taken at the household level, where peer influence from other households in the neighborhood is also taken into account.