Verification through calibration: An approach and a case study of a model of conflict in Syria

Latek, Maciej; Rizi, Seyed M. Mussavi; Geller, Armando

doi:10.1109/wsc.2013.6721547

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…Previous modelling efforts in this area assume perfect knowledge (albeit sometimes with a limited range of perception) and thus optimal decision making (e.g. Frydenlund et al, 2018;Hébert et al, 2018;Łatek et al, 2013;Suleimenova & Groen, 2020). We expect that including the effects of information in these models would change at least some of the observed results.…”

Section: Discussionmentioning

confidence: 95%

The Effects of Information on the Formation of Migration Routes and the Dynamics of Migration

Hinsch

Bijak

2023

Artificial Life

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Most models of migration simply assume that migrants somehow make their way from their point of origin to their chosen destination. We know, however, that—especially in the case of asylum migration—the migrant journey often is a hazardous, difficult process where migrants make decisions based on limited information and under severe material constraints. Here we investigate the dynamics of the migration journey itself using a spatially explicit, agent-based model. In particular we are interested in the effects of limited information and information exchange. We find that under limited information, migration routes generally become suboptimal, their stochasticity increases, and migrants arrive much less frequently at their preferred destination. Under specific circumstances, self-organised consensus routes emerge that are largely unpredictable. Limited information also strongly reduces the migrants’ ability to react to changes in circumstances. We conclude, first, that information and information exchange is likely to have considerable effects on all aspects of migration and should thus be included in future modelling efforts and, second, that there are many questions in theoretical migration research that are likely to profit from the use of agent-based modelling techniques.

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Section: Discussionmentioning

confidence: 95%

The Effects of Information on the Formation of Migration Routes and the Dynamics of Migration

Hinsch

Bijak

2023

Artificial Life

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“…These agents then migrate between the vertices of the graph according to some predefined algorithm. This approach has been wellstudied and utilised in areas such as disaster-driven migration [34], the ongoing conflict in Syria [35], and in [8] applied to conflicts in Burundi, Mali, and the Central African Republic.…”

Section: Discussion: Comparison To Agent-based Modelsmentioning

confidence: 99%

Markov Chain Models of Refugee Migration Data

Huang

Unwin

2019

Preprint

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The application of Markov chains to modelling refugee crises is explored, focusing on local migration of individuals at the level of cities and days. As an explicit example we apply the Markov chains migration model developed here to UNHCR data on the Burundi refugee crisis. We compare our method to a state-of-the-art "agent-based" model of Burundi refugee movements, and highlight that Markov chain approaches presented here can improve the match to data while simultaneously being more algorithmically efficient.

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“…However, their fundamental concern on the lack of data availability arguably becomes less important now as the UNHCR has made refugee data publicly available, and refugees themselves increasingly have access to online platforms. On modelling refugees, Sokolowski et al [8,7] have proposed a detailed agentbased modelling (ABM) framework to model refugees and other factions in the Syrian city of Aleppo, while Latek et al [5] calibrated an ABM tool to model decision-making and dynamic processes around refugees in Syria. Both models are primarily applied for sensitivity analysis, and feature a relatively large number of free parameters.…”

Section: Introductionmentioning

confidence: 99%

Simulating Refugee Movements: Where would You Go?

Groen

2016

Procedia Computer Science

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The challenge of understanding refugee movements is huge and affects countries worldwide on a daily basis. Yet, in terms of simulation, the challenge appears to have been largely ignored. I argue that we as researchers can, and should, harness our computational skills to better understand and predict refugee movements. I reflect on the computational challenges of modelling refugees, and present a simulation case study example focused on the Northern Mali Conflict in 2012. Compared to UNHCR data, the simulation predicts fewer refugees moving towards Mauritania, and more refugees moving towards Niger. This outcome aligns with UNHCR reports, which mention that unregistered refugees were known to reside outside of the official camps, though further investigations are required to rule out competing theories.

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Verification through calibration: An approach and a case study of a model of conflict in Syria

Cited by 6 publications

References 2 publications

The Effects of Information on the Formation of Migration Routes and the Dynamics of Migration

The Effects of Information on the Formation of Migration Routes and the Dynamics of Migration

Markov Chain Models of Refugee Migration Data

Simulating Refugee Movements: Where would You Go?

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