IndShaker: A Knowledge-Based Approach to Enhance Multi-Perspective System Dynamics Analysis

Pileggi, SF

IndShaker: A Knowledge-Based Approach to Enhance Multi-Perspective System Dynamics Analysis

Pileggi, SF

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Modelling, 4, (1), pp. 19-34

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Field	Value	Language
dc.contributor.author	Pileggi, SF
dc.date.accessioned	2022-12-27T14:06:21Z
dc.date.available	2022-12-27T14:06:21Z
dc.identifier.citation	Modelling, 4, (1), pp. 19-34
dc.identifier.issn	2673-3951
dc.identifier.uri	http://hdl.handle.net/10453/164576
dc.description.abstract	<jats:p>Decision making as a result of system dynamics analysis requires, in practice, a straightforward and systematic modeling capability as well as a high-level of customization and flexibility to adapt to situations and environments that may vary very much from each other. While in general terms a completely generic approach could be not as effective as ad hoc solutions, the proper application of modern technology may facilitate agile strategies as a result of a smart combination of qualitative and quantitative aspects. In order to address such complexity, we propose a knowledge-based approach that integrates the systematic computation of heterogeneous criteria with open semantics. The holistic understanding of the framework is described by a reference architecture and the proof-of-concept prototype developed can support high-level system analysis, as well as being suitable within a number of applications contexts—i.e., as a research/educational tool, communication framework, gamification and participatory modeling. Additionally, the knowledge-based philosophy, developed upon Semantic Web technology, increases the capability in terms of holistic knowledge building and re-use via interoperability. Last but not least, the framework is designed to constantly evolve in the next future, for instance by incorporating more advanced AI-powered features.</jats:p>
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Modelling
dc.relation.isbasedon	10.3390/modelling4010002
dc.rights	info:eu-repo/semantics/openAccess
dc.title	IndShaker: A Knowledge-Based Approach to Enhance Multi-Perspective System Dynamics Analysis
dc.type	Journal Article
utslib.citation.volume	4
pubs.organisational-group	/University of Technology Sydney
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 Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2022-12-27T14:06:18Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	4
utslib.citation.issue	1

Abstract:

Decision making as a result of system dynamics analysis requires, in practice, a straightforward and systematic modeling capability as well as a high-level of customization and flexibility to adapt to situations and environments that may vary very much from each other. While in general terms a completely generic approach could be not as effective as ad hoc solutions, the proper application of modern technology may facilitate agile strategies as a result of a smart combination of qualitative and quantitative aspects. In order to address such complexity, we propose a knowledge-based approach that integrates the systematic computation of heterogeneous criteria with open semantics. The holistic understanding of the framework is described by a reference architecture and the proof-of-concept prototype developed can support high-level system analysis, as well as being suitable within a number of applications contexts—i.e., as a research/educational tool, communication framework, gamification and participatory modeling. Additionally, the knowledge-based philosophy, developed upon Semantic Web technology, increases the capability in terms of holistic knowledge building and re-use via interoperability. Last but not least, the framework is designed to constantly evolve in the next future, for instance by incorporating more advanced AI-powered features.

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http://hdl.handle.net/10453/164576