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.
Highlights
Agent-based models (ABMs) are characterized by a set of rules, typically compiled together in a computer program, which describe the evolution of the model in time from an initial condition
We focus on using the ensemble Kalman filter (EnKF), as ABMs are often highly nonlinear and can be computationally intensive
We have considered how dynamic data assimilation (DDA) techniques, in particular the EnKF, can be applied to ABMs
Summary
Agent-based models (ABMs) are characterized by a set of rules, typically compiled together in a computer program, which describe the evolution of the model in time from an initial condition. While agent-based systems can be used for a wide variety of purposes, including systems control, Web-search and robotics [1], ABMs are commonly implemented in science to understand social and environmental systems, either as simple in silico thought experiments or, increasingly, as detailed models of the real world [2] In the latter case, the aim is not always. The rise of Web-based services, cheap field sensors and individual-level data collection and collation has recently led to a much greater availability of individual-level and environmental data, much of it streamed over near-continuous time Such data offer the opportunity for models that use dynamic data assimilation (DDA) to constrain their continued predictive evolution against the real world [6].
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