Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/22565
Title: Sensitivity-driven simulation development: a case study in forced migration
Authors: Suleimenova, D
Arabnejad, H
Edeling, WN
Groen, D
Keywords: uncertainty quantification;sensitivity analysis;simulation development approach;agent-based modelling;forced migration prediction
Issue Date: 29-Mar-2021
Publisher: The Royal Society Publishing
Citation: Suleimenova, D., Arabnejad, H., Edeling, W.N. and Groen, D. (2021) 'Sensitivity-driven simulation development: a case study in forced migration', Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 379 (2197), 20200077, pp. 1 - 18. doi: 10.1098/rsta.2020.0077.
Abstract: © 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'.
URI: https://bura.brunel.ac.uk/handle/2438/22565
DOI: https://doi.org/10.1098/rsta.2020.0077
ISSN: 1364-503X
Appears in Collections:Dept of Computer Science Research Papers

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