Ideal, best, and emerging practices in creating artificial societies

Giabbanelli, PJ; Castellani, B; Voinov, AA; Törnberg, P

Ideal, best, and emerging practices in creating artificial societies

Giabbanelli, PJ Castellani, B Voinov, AA Törnberg, P

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Simulation Series, 2019, 51, (1), pp. 1-12
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Giabbanelli, PJ
dc.contributor.author	Castellani, B
dc.contributor.author	Voinov, AA
dc.contributor.author	Törnberg, P
dc.date	2019-04-29
dc.date.accessioned	2020-06-01T21:50:06Z
dc.date.available	2020-06-01T21:50:06Z
dc.date.issued	2019-01-01
dc.identifier.citation	Simulation Series, 2019, 51, (1), pp. 1-12
dc.identifier.isbn	9781510892569
dc.identifier.issn	0735-9276
dc.identifier.uri	http://hdl.handle.net/10453/141028
dc.description.abstract	© 2019 Society for Modeling & Simulation International (SCS). Artificial societies used to guide and evaluate policies should be built by following “best practices”. However, this goal may be challenged by the complexity of artificial societies and the interdependence of their sub-systems (e.g., built environment, social norms). We created a list of seven practices based on simulation methods, specific aspects of quantitative individual models, and data-driven modeling. By evaluating published models for public health with respect to these ideal practices, we noted significant gaps between current and ideal practices on key items such as replicability and uncertainty. We outlined opportunities to address such gaps, such as integrative models and advances in the computational machinery used to build simulations.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Simulation Series
dc.relation.ispartof	Spring Simulation Conference
dc.relation.isbasedon	10.23919/SpringSim.2019.8732881
dc.rights	info:eu-repo/semantics/closedAccess
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Ideal, best, and emerging practices in creating artificial societies
dc.type	Conference Proceeding
utslib.citation.volume	51
utslib.location.activity	Tucson, AZ, USA
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - PERSWADE - Centre on Persuasive Systems for Wise Adaptive Living
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-01T21:50:04Z
pubs.finish-date	2019-05-02
pubs.issue	1
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-04-29
pubs.volume	51
utslib.start-page	1
utslib.citation.issue	1
dc.location	Piscataway, USA

Abstract:

© 2019 Society for Modeling & Simulation International (SCS). Artificial societies used to guide and evaluate policies should be built by following “best practices”. However, this goal may be challenged by the complexity of artificial societies and the interdependence of their sub-systems (e.g., built environment, social norms). We created a list of seven practices based on simulation methods, specific aspects of quantitative individual models, and data-driven modeling. By evaluating published models for public health with respect to these ideal practices, we noted significant gaps between current and ideal practices on key items such as replicability and uncertainty. We outlined opportunities to address such gaps, such as integrative models and advances in the computational machinery used to build simulations.

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http://hdl.handle.net/10453/141028