Calibrating Agent-Based Models Using Uncertainty Quantification Methods

McCulloch, J, Ge, J, Ward, JA et al. (3 more authors) (2022) Calibrating Agent-Based Models Using Uncertainty Quantification Methods. Journal of Artificial Societies and Social Simulation, 25 (2). 1. ISSN 1460-7425



Copyright, Publisher and Additional Information: This is protected by copyright. All rights reserved. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0)
Keywords: Calibration, Optimisation, History Matching, Proximate Bayesian Computation, Uncertainty, Agent-Based Modelling
  • Accepted: 29 January 2022
  • Published (online): 31 March 2022
  • Published: 31 March 2022
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 05 Apr 2022 13:28
Last Modified: 05 Apr 2022 13:28
Status: Published
Publisher: SimSoc Consortium
Identification Number:


Filename: 1.pdf

Licence: CC-BY 4.0

Share / Export