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R Soc Open Sci. 2016 Apr 13;3(4):150703. doi: 10.1098/rsos.150703. eCollection 2016 Apr.

Dynamic calibration of agent-based models using data assimilation.

Royal Society open science

Jonathan A Ward, Andrew J Evans, Nicolas S Malleson

Affiliations

  1. School of Mathematics, University of Leeds , Leeds LS2 9JT, UK.
  2. School of Geography, University of Leeds , Leeds LS2 9JT, UK.

PMID: 27152214 PMCID: PMC4852637 DOI: 10.1098/rsos.150703

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.

Keywords: agent-based models; complex systems; data assimilation

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