Modelling Migration: Decisions, Processes and Outcomes

Bijak, Jakub; Higham, Philip A.; Hilton, Jason; Hinsch, Martin; Nurse, Sarah; Prike, Toby; Smith, Peter; Reinhardt, Oliver; Uhrmacher, Adelinde M.

doi:10.1109/wsc48552.2020.9384072

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…To address these limitations, we propose ABSCIM (Agent-based simulator for conflict-induced migration), a data-driven, theory-guided, agent-based framework to model forced migration. Necessity of the data-driven part of the framework arises from the fact that uncertainty and noise are always lingering issues associated with data regarding forced migration events ( 6 , 16 ) and uncertainty remains with respect to the kind of data essential to drive the framework. We take a step towards filling this gap by combining real-world conflict information with digital twins ( 17 , 18 ) in the form of a synthetic population.…”

Section: Introductionmentioning

confidence: 99%

“…To address this, we embed necessary social rules to the decision process of the synthetic individuals when they interact with the real-world conflict events. Furthermore, because migration is ultimately a macro-level phenomenon resulting from the aggregation of micro-level individual decisions ( 16 ), an agent-based model (ABM) is suitable for mimicking this bottom-up behavior. By embedding the decision-making process of each synthetic individual as rules derived from social theories and having them react to real-world events under the hood of an ABM, we can obtain meaningful results.…”

Section: Introductionmentioning

confidence: 99%

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

Mehrab,

Stundal,

Venkatramanan

et al. 2024

PNAS Nexus

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The ongoing Russian aggression against Ukraine has forced over eight million people to migrate out of Ukraine. Understanding the dynamics of forced migration is essential for policy-making and for delivering humanitarian assistance. Existing work is hindered by a reliance on observational data which is only available well after the fact. In this work, we study the efficacy of a data-driven agent-based framework motivated by social and behavioral theory in predicting outflow of migrants as a result of conflict events during the initial phase of the Ukraine war. We discuss policy use cases for the proposed framework by demonstrating how it can leverage refugee demographic details to answer pressing policy questions. We also show how to incorporate conflict forecast scenarios to predict future conflict-induced migration flows. Detailed future migration estimates across various conflict scenarios can both help to reduce policymaker uncertainty and improve allocation and staging of limited humanitarian resources in crisis settings.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Mehrab,

Stundal,

Venkatramanan

et al. 2024

PNAS Nexus

View full text Add to dashboard Cite

show abstract

“…Due to these issues, we approach this problem using the agent-based modeling (ABM) technique, which implicitly makes several AI methodologies and topical areas applicable in our context. ABM is well-suited for studying the problems where decisions and interactions of individuals result in an aggregated phenomenon (Asgary, Solis et al 2016;Bijak, Higham et al 2020). Thus, ABM can be used to observe the dynamics of emergent social phenomena in the advent of shock events (Social Simulation).…”

Section: Introductionmentioning

confidence: 99%

A Generalizable Theory-Driven Agent-Based Framework to Study Conflict-Induced Forced Migration

Mehrab,

Stundal,

Venkatramanan

et al. 2024

AAAI

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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.

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Uncertainty Quantification, Model Calibration and Sensitivity

Bijak

Hilton

2021

Methodos Series

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Better understanding of the behaviour of agent-based models, aimed at embedding them in the broader, model-based line of scientific enquiry, requires a comprehensive framework for analysing their results. Seeing models as tools for experimenting in silico, this chapter discusses the basic tenets and techniques of uncertainty quantification and experimental design, both of which can help shed light on the workings of complex systems embedded in computational models. In particular, we look at: relationships between model inputs and outputs, various types of experimental design, methods of analysis of simulation results, assessment of model uncertainty and sensitivity, which helps identify the parts of the model that matter in the experiments, as well as statistical tools for calibrating models to the available data. We focus on the role of emulators, or meta-models – high-level statistical models approximating the behaviour of the agent-based models under study – and in particular, on Gaussian processes (GPs). The theoretical discussion is illustrated by applications to the Routes and Rumours model of migrant route formation introduced before.

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Modelling Migration: Decisions, Processes and Outcomes

Cited by 5 publications

References 27 publications

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

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

A Generalizable Theory-Driven Agent-Based Framework to Study Conflict-Induced Forced Migration

Uncertainty Quantification, Model Calibration and Sensitivity

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