Towards an automated framework for agent-based simulation of refugee movements

© 2017 IEEE. Forced migration is a growing global problem, and the world now has a record amount of 22.5 million refugees. Models that predict refugee movements are few and far between, and constructing these models requires a substantial amount of manual effort while erupting refugee crises require a very rapid response. Here we present a vision towards establishing an automated framework, aimed to enable researchers to construct simulations of refugee movements more quickly and systematically. Our approach incorporates a diverse range of data sources, and uses the FabSim toolkit in conjunction with the Flee simulation code to quickly generate simulation workflows. In addition, we highlight a few key steps that we have already taken towards realizing this vision and discuss opportunities for wider applicability

Predicting forced displacement using a generalised and automated agent-based simulation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWithin the last decades, international migration demonstrated an escalating growth with more than 68 million people forcibly displaced worldwide. Forced displacement has a huge impact on society today as 40 million people internally displaced within their home country and 25.4 millionrefugeesfledtoneighbouringcountries. Forcibly displaced people face several concerns, namely, the choice to stay or flee, the choice to flee internally or across borders, and the choice of destination. These decisions are often based on economic, social and/or political push and pull factors in origin and destination countries. Existing international migration theories frequently cover specific aspects of migration processes, such as why human migration occurs and what effects it has on economies. However, the combination of all the factors and reasons for human movement requires expertise from various disciplines at once. Moreover, existing migration theories are not extensive enough for practical applications, statistical methods are outdated, and usually, do not account for forced population movements. To fulfil gaps within forced displacement predictions, we use computational models as they can contribute to a better understanding of forced displacement patterns and have potential due to their reduced ethical burden. We propose a generalised simulation development approach (SDA) to predict forced population movements in conflict regions. Our SDA consists of a systematic set of phases to build agent-based simulations, which includes a generic model to define a real system problem, and simulation development and validation for situation-specific scenarios. We also synthesise data from UNHCR, ACLED and Bing Maps to build and validate agent-based simulations of three major African conflicts, namely Burundi, Central African Republic and Mali, and predict the distribution of incoming forced migrants across destination camps. Our simulations consistently predict more than 75% of the population arrivals in camps correctly after the first 12 days. Our agent-based simulation tool can help save migrants’ lives by allowing governments and NGOs to conduct a better-informed allocation of humanitarian resources. Few researchers have investigated the effects of policy decisions, such as camp capacity changes, camp and border closures and forced redirection, on forced population movements. To make such a study accurate and feasible in terms of human effort, we automate our generalised SDA by introducing and applying the FabFlee automation toolkit. We use our automated SDA to analyse the South Sudan crisis by incorporating two capacity changes to Adjumani camp, a border closure between South Sudan and Uganda, and forced redirection between Ethiopian camps. We find that a reduction in camp capacity induces up to 16% fewer forced population arrivals while an increase in camp capacity results in a limited increase in forced population arrivals (< 4%) at the destination camps. In addition, border closure results in 40% fewer force population arrivals and an increasingly long travel journey to other camps. There is also a lingering effect in prolonged force population journey times once a border is again reopened and a clear boost in forced population arrivals when forced population are redirected to a reduced number of camps with larger capacities. To the best of our knowledge, we are the first to conduct such an investigation for forced displacement conflict situations

A coupled food security and refugee movement model for the south Sudan conflict

VECMA; HiDALGO projects; European Union Horizon 2020 research and innovation programm

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Sensitivity-driven simulation development: a case study in forced migration

© 2021 The Authors. This paper presents an approach named sensitivity-driven simulation development (SDSD), where the use of sensitivity analysis (SA) guides the focus of further simulation development and refinement efforts, avoiding direct calibration to validation data. SA identifies assumptions that are particularly pivotal to the validation result, and in response model ruleset refinement resolves those assumptions in greater detail, balancing the sensitivity more evenly across the different assumptions and parameters. We implement and demonstrate our approach to refine agent-based models of forcibly displaced people in neighbouring countries. Over 70.8 million people are forcibly displaced worldwide, of which 26 million are refugees fleeing from armed conflicts, violence, natural disaster or famine. Predicting forced migration movements is important today, as it can help governments and NGOs to effectively assist vulnerable migrants and efficiently allocate humanitarian resources. We use an initial SA iteration to steer the simulation development process and identify several pivotal parameters. We then show that we are able to reduce the relative sensitivity of these parameters in a secondary SA iteration by approximately 54% on average. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.European Union Horizon 2020 research and innovation programme VECMA and HiDALGO projects under grant agreement nos 800925 and 824115

Computational Conflict Research

This open access book brings together a set of original studies that use cutting-edge computational methods to investigate conflict at various geographic scales and degrees of intensity and violence. Methodologically, this book covers a variety of computational approaches from text mining and machine learning to agent-based modelling and social network analysis. Empirical cases range from migration policy framing in North America and street protests in Iran to violence against civilians in Congo and food riots world-wide. Supplementary materials in the book include a comprehensive list of the datasets on conflict and dissent, as well as resources to online repositories where the annotated code and data of individual chapters can be found and where (agent-based) models can be re-produced and altered. These materials are a valuable resource for those wishing to retrace and learn from the analyses described in this volume and adapt and apply them to their own research interests. By bringing together novel research through an international team of scholars from a range of disciplines, Computational Conflict Research pioneers and maps this emerging field. The book will appeal to students, scholars, and anyone interested in the prospects of using computational social sciences to advance our understanding of conflict dynamics