A generalized simulation development approach for predicting refugee destinations

© 2017, The Author(s). In recent years, global forced displacement has reached record levels, with 22.5 million refugees worldwide. Forecasting refugee movements is important, as accurate predictions can help save refugee lives by allowing governments and NGOs to conduct a better informed allocation of humanitarian resources. Here, we propose a generalized simulation development approach to predict the destinations of refugee movements in conflict regions. In this approach, we synthesize data from UNHCR, ACLED and Bing Maps to construct agent-based simulations of refugee movements. We apply our approach to develop, run and validate refugee movement simulations set in three major African conflicts, estimating the distribution of incoming refugees across destination camps, given the expected total number of refugees in the conflict. Our simulations consistently predict more than 75% of the refugee destinations correctly after the first 12 days, and consistently outperform alternative naive forecasting techniques. Using our approach, we are also able to reproduce key trends in refugee arrival rates found in the UNHCR data

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

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

The World of the Russian Word and its Value Content

В настоящей работе идет речь о системе ценностей русской языковой картины мира, благодаря которой процесс межкультурной коммуникации в рамках педагогического взаимодействия между преподавателем и обучающимися становится более успешным.Цель работы – описание основных ценностей (ОЦ) русской культуры, в частности, концепта «ЛЮБОВЬ». Используя методы психолингвистики и возможности корпусной лингвистики, автор дифференцирует базовые для русского языкового сознания семы исследуемого конструкта. Результаты статьи будут полезны для теории межкультурной коммуникации, в практике РКИ, при методической выборке текстов для чтения и их обсуждения в аудитории.This paper deals with the system of values of the Russian language picture of the world, thanks to which the process of intercultural communication within the framework of the pedagogical interaction between the teacher and the students becomes more successful.The purpose of the work is to describe the core values (OC) of Russian culture, in particular, the concept of “LOVE”. Using the methods of psycholinguistics and corpus linguistics, the author differentiates the components of the studied construct, which are basic for the Russian language consciousness. The results of the article will be useful in the practice of practice of language teaching, with a methodical selection of texts for reading and discussion in the audience, within the framework of the theory of intercultural communication

Facilitating simulation development for global challenge response and anticipation in a timely way

Data availability: No data was used for the research described in the article.Copyright © 2023 The Author(s). An important subset of today’s global crises, such as the 2015 migration crisis in Syria and the 2020 COVID pandemic, has a rapid and hard-to-extrapolate evolution that complicates the preparation of a community response. Simulation-based forecasts for such crises can help to guide the selection or development of mitigation policies or inform the efficient allocation of support resources. However, the time required to develop, execute and validate these models can often be intractably long, causing many of these forecasts to only become accurate after the damage has already occurred. In this paper, we present a generic simulation development approach (or SDA) to tackle this challenge. It consists of three important phases: identifying anticipatory activities required for developing application-agnostic modelling tools, identifying activities required to adapt these models to address specific (global) challenges, and automating a large subset of the aforementioned activities using existing software tool. Here, a key aspect is to ensure that our models are reliable: this involves a range of tasks for validation, ensemble forecasting, uncertainty quantification and sensitivity analysis. To showcase the added value of a generic simulation development approach, we present and discuss two specific applications of this approach: one in the context of modelling conflict-driven migration and one in the context of modelling the spread of COVID-19.This work is supported by the ITFLOWS, HiDALGO and STAMINA projects, which have received funding from the European Union Horizon 2020 research and innovation programme under grant agreement nos 882986, 824115 and 883441. This work has also been supported by the SEAVEA ExCALIBUR project, which has received funding from EPSRC under grant agreement EP/W007711/1

FabSim3: An automation toolkit for verified simulations using high performance computing

A common feature of computational modelling and simulation research is the need to perform many tasks in complex sequences to achieve a usable result. This will typically involve tasks such as preparing input data, pre-processing, running simulations on a local or remote machine, post-processing, and performing coupling communications, validations and/or optimisations. Tasks like these can involve manual steps which are time and effort intensive, especially when it involves the management of large ensemble runs. Additionally, human errors become more likely and numerous as the research work becomes more complex, increasing the risk of damaging the credibility of simulation results. Automation tools can help ensure the credibility of simulation results by reducing the manual time and effort required to perform these research tasks, by making more rigorous procedures tractable, and by reducing the probability of human error due to a reduced number of manual actions. In addition, efficiency gained through automation can help researchers to perform more research within the budget and effort constraints imposed by their projects. This paper presents the main software release of FabSim3, and explains how our automation toolkit can improve and simplify a range of tasks for researchers and application developers. FabSim3 helps to prepare, submit, execute, retrieve, and analyze simulation workflows. By providing a suitable level of abstraction, FabSim3 reduces the complexity of setting up and managing a large-scale simulation scenario, while still providing transparent access to the underlying layers for effective debugging. The tool also facilitates job submission and management (including staging and curation of files and environments) for a range of different supercomputing environments. Although FabSim3 itself is application-agnostic, it supports a provably extensible plugin system where users automate simulation and analysis workflows for their own application domains. To highlight this, we briefly describe a selection of these plugins and we demonstrate the efficiency of the toolkit in handling large ensemble workflows.EPSRC under grant agreement EP/W007711/1, as well as by the VECMA and HiDALGO projects, which have received funding from the European Union Horizon 2020 research and innovation programme under grant agreement nos 800925 and 824115. In addition, FabFlee was supported by the ITFLOWS project and FabCovid19 by the STAMINA project, both of which have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 882986 and No 883441 respectivel

Sensitivity Analysis of High-Dimensional Models with Correlated Inputs

The file archived on this repository is a preprint. It has not been certified by peer reviewSensitivity analysis is an important tool used in many domains of computational science to either gain insight into the mathematical model and interaction of its parameters or study the uncertainty propagation through the input-output interactions. In many applications, the inputs are stochastically dependent, which violates one of the essential assumptions in the state-of-the-art sensitivity analysis methods. Consequently, the results obtained ignoring the correlations provide values which do not reflect the true contributions of the input parameters. This study proposes an approach to address the parameter correlations using a polynomial chaos expansion method and Rosenblatt and Cholesky transformations to reflect the parameter dependencies. Treatment of the correlated variables is discussed in context of variance and derivative-based sensitivity analysis. We demonstrate that the sensitivity of the correlated parameters can not only differ in magnitude, but even the sign of the derivative-based index can be inverted, thus significantly altering the model behavior compared to the prediction of the analysis disregarding the correlations. Numerous experiments are conducted using workflow automation tools within the VECMA toolkit.This research is part of the activities of the Innosuisse project no 34394.1 entitled “High-Performance Data Analytics Framework for Power Markets Simulation” which is financially supported by the Swiss Innovation Agency. This work was supported by a grant from the Swiss National Supercomputing Centre (CSCS) under project ID d120. DS and DG have been supported by the SEAVEA ExCALIBUR project, which has received funding from EPSRC under grant agreement EP/W007711/1