Machine-assisted agent-based modeling: Opening the black box

Taghikhah, F; Voinov, A; Filatova, T; Polhill, JG

Machine-assisted agent-based modeling: Opening the black box

Taghikhah, F

Voinov, A Filatova, T Polhill, JG

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Journal of Computational Science, 2022, 64
Issue Date:: 2022-10-01

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Field	Value	Language
dc.contributor.author	Taghikhah, F https://orcid.org/0000-0002-0851-6816
dc.contributor.author	Voinov, A
dc.contributor.author	Filatova, T
dc.contributor.author	Polhill, JG
dc.date.accessioned	2023-07-02T23:57:20Z
dc.date.available	2023-07-02T23:57:20Z
dc.date.issued	2022-10-01
dc.identifier.citation	Journal of Computational Science, 2022, 64
dc.identifier.issn	1877-7503
dc.identifier.issn	1877-7511
dc.identifier.uri	http://hdl.handle.net/10453/171115
dc.description.abstract	While agent-based modeling (ABM) has become one of the most powerful tools in quantitative social sciences, it remains difficult to explain their structure and performance. We propose to use artificial intelligence both to build the models from data, and to improve the way we communicate models to stakeholders. Although machine learning is actively employed for pre-processing data, here for the first time, we used it to facilitate model development of a simulation model directly from data. Our suggested framework, ML-ABM accounts for causality and feedback loops in a complex nonlinear system and at the same time keeps it transparent for stakeholders. As a result, beside the development of a behavioral ABM, we open the ‘blackbox’ of purely empirical models. With our approach, artificial intelligence in the simulation field can open a new stream in modeling practices and provide insights for future applications.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Journal of Computational Science
dc.relation.isbasedon	10.1016/j.jocs.2022.101854
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0802 Computation Theory and Mathematics, 0806 Information Systems
dc.title	Machine-assisted agent-based modeling: Opening the black box
dc.type	Journal Article
utslib.citation.volume	64
utslib.for	0802 Computation Theory and Mathematics
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Arts and Social Sciences
pubs.organisational-group	/University of Technology Sydney/Faculty of Arts and Social Sciences/School of Communication
utslib.copyright.status	closed_access	*
dc.date.updated	2023-07-02T23:56:25Z
pubs.publication-status	Published
pubs.volume	64

Abstract:

While agent-based modeling (ABM) has become one of the most powerful tools in quantitative social sciences, it remains difficult to explain their structure and performance. We propose to use artificial intelligence both to build the models from data, and to improve the way we communicate models to stakeholders. Although machine learning is actively employed for pre-processing data, here for the first time, we used it to facilitate model development of a simulation model directly from data. Our suggested framework, ML-ABM accounts for causality and feedback loops in a complex nonlinear system and at the same time keeps it transparent for stakeholders. As a result, beside the development of a behavioral ABM, we open the ‘blackbox’ of purely empirical models. With our approach, artificial intelligence in the simulation field can open a new stream in modeling practices and provide insights for future applications.

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http://hdl.handle.net/10453/171115