Dynamic calibration of agent-based models using data assimilation
Authored by Jonathan A Ward, Andrew J Evans, Nicolas S Malleson
Date Published: 2016
DOI: 10.1098/rsos.150703
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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.
Tags
Simulation
Demand
Pedestrian route-choice
Retail facilities