Ward, JA orcid.org/0000-0002-2469-7768, Evans, A orcid.org/0000-0002-3524-1571 and Malleson, N orcid.org/0000-0002-6977-0615 (2016) Dynamic calibration of agent-based models using data assimilation. Royal Society Open Science, 3 (4). 150703. ISSN 2054-5703
Abstract
A widespread approach to investigating the dynamical behaviour of complex social systems is via agent-based models (ABMs). In this paper, we describe how such models can be dynamically calibrated using the ensemble Kalman filter (EnKF), a standard method of data assimilation. Our goal is twofold. First, we want to present the EnKF in a simple setting for the benefit of ABM practitioners who are unfamiliar with it. Second, we want to illustrate to data assimilation experts the value of using such methods in the context of ABMs of complex social systems and the new challenges these types of model present. We work towards these goals within the context of a simple question of practical value: how many people are there in Leeds (or any other major city) right now? We build a hierarchy of exemplar models that we use to demonstrate how to apply the EnKF and calibrate these using open data of footfall counts in Leeds.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2016 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
Keywords: | Agent Based Models; Data Assimilation; Complex Systems |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Applied Mathematics (Leeds) The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Mar 2016 14:07 |
Last Modified: | 16 Apr 2021 11:03 |
Published Version: | http://doi.org/10.1098/rsos.150703 |
Status: | Published |
Publisher: | The Royal Society |
Identification Number: | 10.1098/rsos.150703 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:96481 |