Calibrating agent-based models using uncertainty quantification methods

McCulloch, J., Ge, J., Ward, J. A., Heppenstall, A. , Polhill, J. G. and Malleson, N. (2022) Calibrating agent-based models using uncertainty quantification methods. Journal of Artificial Societies and Social Simulation, 25(2), 1. (doi: 10.18564/jasss.4791)

Abstract

Agent-based models (ABMs) can be found across a number of diverse application areas ranging from simulating consumer behaviour to infectious disease modelling. Part of their popularity is due to their ability to simulate individual behaviours and decisions over space and time. However, whilst there are plentiful examples within the academic literature, these models are only beginning to make an impact within policy areas. Whilst frameworks such as NetLogo make the creation of ABMs relatively easy, a number of key methodological issues, including the quantification of uncertainty, remain. In this paper we draw on state-of-the-art approaches from the fields of uncertainty quantification and model optimisation to describe a novel framework for the calibration of ABMs using History Matching and Approximate Bayesian Computation. The utility of the framework is demonstrated on three example models of increasing complexity: (i) Sugarscape to illustrate the approach on a toy example; (ii) a model of the movement of birds to explore the efficacy of our framework and compare it to alternative calibration approaches and; (iii) the RISC model of farmer decision making to demonstrate its value in a real application. The results highlight the efficiency and accuracy with which this approach can be used to calibrate ABMs. This method can readily be applied to local or national-scale ABMs, such as those linked to the creation or tailoring of key policy decisions.

• Item Type: Articles
• Additional Information: This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 757455) and was supported by The Alan Turing Institute. AH was also supported by grants from UKPRP (MR/S037578/2), Medical Research Council (MC_UU_00022/5) and Scottish Government Chief Scientist Office (SPHSU20).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Heppenstall, Professor Alison
• Authors: McCulloch, J., Ge, J., Ward, J. A., Heppenstall, A., Polhill, J. G., and Malleson, N.
• College/School: College of Social Sciences > School of Social and Political Sciences; College of Social Sciences > School of Social and Political Sciences > Urban Studies
• Journal Name: Journal of Artificial Societies and Social Simulation
• Publisher: University of Surrey
• ISSN: 1460-7425
• ISSN (Online): 1460-7425
• Copyright Holders: Copyright © 2022 Journal of Artificial Societies and Social Simulation
• First Published: First published in Journal of Artificial Societies and Social Simulation 25(2):1
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
304823	1	Systems science research in public health	Petra Meier	Medical Research Council (MRC)	MC_UU_00022/5	HW - MRC/CSO Social and Public Health Sciences Unit
313944		System-science Informed Public Health and Economic Research for non-communicable Disease Prevention (the SIPHER consortium)	Petra Meier	Medical Research Council (MRC)	MR/S037578/2	HW - MRC/CSO Social and Public Health Sciences Unit
304823	1	Systems science research in public health	Petra Meier	Chief Scientist Office (CSO)	SPHSU20	HW - MRC/CSO Social and Public Health Sciences Unit