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DC Field | Value | Language |
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dc.contributor.author | Geiger, B | - |
dc.contributor.author | Jahani, A | - |
dc.contributor.author | Hussain, H | - |
dc.contributor.author | Groen, D | - |
dc.coverage.spatial | London, UK | - |
dc.date.accessioned | 2023-07-04T20:01:35Z | - |
dc.date.available | 2023-07-04T20:01:35Z | - |
dc.date.issued | 2023-06-02 | - |
dc.identifier | ORCID iDs: Alireza Jahani https://orcid.org/0000-0001-9813-352X; Derek Groen https://orcid.org/0000-0001-7463-3765. | - |
dc.identifier.citation | Geiger, B. et al. (2023) 'Markov Aggregation for Speeding Up Agent-Based Movement Simulations', Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, London, UK, 29 May - 2 June, pp. 1877 - 1885. Available at: https://www.southampton.ac.uk/~eg/AAMAS2023/pdfs/p1877.pdf | en_US |
dc.identifier.isbn | 978-1-4503-9432-1 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/26775 | - |
dc.description | ... | en_US |
dc.description.abstract | In this work, we investigate Markov aggregation for agent-based models (ABMs). Specifically, if the ABM models agent movements on a graph, if its ruleset satisfies certain assumptions, and if the aim is to simulate aggregate statistics such as vertex populations, then the ABM can be replaced by a Markov chain on a comparably small state space. This equivalence between a function of the ABM and a smaller Markov chain allows to reduce the computational complexity of the agent-based simulation from being linear in the number of agents, to being constant in the number of agents and polynomial in the number of locations. We instantiate our theory for a recent ABM for forced migration (Flee).We show that,even though the rulesets of Flee violate some of our necessary assumptions, the aggregated Markov chain-based model,Markov Flee,achieves comparable accuracy at substantially reduced computational cost. Thus, Markov Flee can help NGOs and policy makers forecast forced migration in certain conflict scenarios in a cost-effective manner, contributing to fast and efficient delivery of humanitarian relief. | en_US |
dc.description.sponsorship | This work has been supported by the HiDALGO, ITFLOWS, SEAVEA ExCALIBUR, and BrAIN projects. The projects HiDALGO (Grant No. 824115) and ITFLOWS (Grant No. 882986) have been funded by the European Commission’s H2020 Programme. The project SEAVEA ExCALIBUR (Grant No. EP/W007711/1) has received funding from EPSRC. The project BrAIN – Brownfield Artificial Intelligence Network for Forging of High Quality Aerospace Components (Grant No. 881039) is funded in the framework of the program “TAKE OFF”, which is a research and technology program of the Austrian Federal Ministry of Transport, Innovation and Technology. The Know-Center is funded within the Austrian COMET Program - Competence Centers for Excellent Technologies - under the auspices of the Austrian Federal Ministry of Climate Action, Environment, Energy, Mobility, Innovation and Technology, the Austrian Federal Ministry of Digital and Economic Affairs, and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency FFG. | en_US |
dc.format.extent | 1877 - 1885 | - |
dc.language.iso | en_US | en_US |
dc.publisher | International Foundation for Autonomous Agents and Multiagent Systems | en_US |
dc.source | 2023 International Conference on Autonomous Agents and Multiagent Systems | - |
dc.source | 2023 International Conference on Autonomous Agents and Multiagent Systems | - |
dc.subject | agent-based model | en_US |
dc.subject | Markov chains | en_US |
dc.subject | model reduction | en_US |
dc.subject | social simulation | en_US |
dc.title | Markov Aggregation for Speeding Up Agent-Based Movement Simulations | en_US |
dc.type | Conference Paper | en_US |
dc.relation.isPartOf | Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems | - |
pubs.finish-date | 2023-06-02 | - |
pubs.finish-date | 2023-06-02 | - |
pubs.publication-status | Published | - |
pubs.start-date | 2023-05-29 | - |
pubs.start-date | 2023-05-29 | - |
Appears in Collections: | Dept of Computer Science Research Papers |
Files in This Item:
File | Description | Size | Format | |
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FullText.pdf | 1.79 MB | Adobe PDF | View/Open |
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