Metasynthesis: M-space, M-interaction, and M-computing for open complex giant systems

Cao, L; Dai, R; Zhou, M

Metasynthesis: M-space, M-interaction, and M-computing for open complex giant systems

Cao, L

Dai, R Zhou, M

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 2009, 39 (5), pp. 1007 - 1021
Issue Date:: 2009-07-16

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Field	Value	Language
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Dai, R	en_US
dc.contributor.author	Zhou, M	en_US
dc.date.issued	2009-07-16	en_US
dc.identifier.citation	IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 2009, 39 (5), pp. 1007 - 1021	en_US
dc.identifier.issn	1083-4427	en_US
dc.identifier.uri	http://hdl.handle.net/10453/8927
dc.description.abstract	The studies of complex systems have been recognized as one of the greatest challenges for current and future science and technology. Open complex giant systems (OCGSs) are a family of specially complex systems with system complexities such as openness, human involvement, societal characteristic, and intelligence emergence. They greatly challenge multiple disciplines such as system sciences, system engineering, cognitive sciences, information systems, artificial intelligence, and computer sciences. As a result, traditional problem-solving methodologies can help deal with them but are far from a mature solution methodology. The theory of qualitative-to-quantitative metasynthesis has been proposed as a breakthrough and effective methodology for the understanding and problem solving of OCGSs. In this paper, we propose the concepts of M-Interaction, M-Space, and M-Computing which are three key components for studying OCGS and building problem-solving systems. M-Interaction forms the main problem-solving mechanism of qualitative-to-quantitative metasynthesis; M-Space is the OCGS problem-solving system embedded with M-Interactions, while M-Computing consists of engineering approaches to the analysis, design, and implementation of M-Space and M-Interaction. We discuss the theoretical framework, problem-solving process, social cognitive evolution, intelligence emergence, and pitfalls of certain types of cognitions in developing M-Space and M-Interaction from the perspectives of cognitive sciences and social cognitive interaction. These can help one understand complex systems and develop effective problem-solving methodologies. © 2009 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans	en_US
dc.relation.isbasedon	10.1109/TSMCA.2009.2022414	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Metasynthesis: M-space, M-interaction, and M-computing for open complex giant systems	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	39	en_US
utslib.for	080602 Computer-Human Interaction	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
dc.location.activity	ISI:000269155600006	en_US
pubs.embargo.period	Not known	en_US
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 - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	39	en_US

Abstract:

The studies of complex systems have been recognized as one of the greatest challenges for current and future science and technology. Open complex giant systems (OCGSs) are a family of specially complex systems with system complexities such as openness, human involvement, societal characteristic, and intelligence emergence. They greatly challenge multiple disciplines such as system sciences, system engineering, cognitive sciences, information systems, artificial intelligence, and computer sciences. As a result, traditional problem-solving methodologies can help deal with them but are far from a mature solution methodology. The theory of qualitative-to-quantitative metasynthesis has been proposed as a breakthrough and effective methodology for the understanding and problem solving of OCGSs. In this paper, we propose the concepts of M-Interaction, M-Space, and M-Computing which are three key components for studying OCGS and building problem-solving systems. M-Interaction forms the main problem-solving mechanism of qualitative-to-quantitative metasynthesis; M-Space is the OCGS problem-solving system embedded with M-Interactions, while M-Computing consists of engineering approaches to the analysis, design, and implementation of M-Space and M-Interaction. We discuss the theoretical framework, problem-solving process, social cognitive evolution, intelligence emergence, and pitfalls of certain types of cognitions in developing M-Space and M-Interaction from the perspectives of cognitive sciences and social cognitive interaction. These can help one understand complex systems and develop effective problem-solving methodologies. © 2009 IEEE.

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http://hdl.handle.net/10453/8927