Consensus reaching with non-cooperative behavior management for personalized individual semantics-based social network group decision making

Gespeichert in:

Autor*in:	Gao, Yuan [verfasserIn] Zhang, Zhen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	social network analysis group decision making consensus reaching non-cooperative behavior personalized individual semantics

Übergeordnetes Werk:	Enthalten in: Journal of the Operational Research Society - Operational Research Society, London : Taylor and Francis, 1978, 73(2022), 11, Seite 2518-2535
Übergeordnetes Werk:	volume:73 ; year:2022 ; number:11 ; pages:2518-2535

Links:	Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	10.1080/01605682.2021.1997654

Katalog-ID:	1833712080

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982			\|2 26 \|1 00 \|x DE-206 \|b Leveraging social network trust relationships among experts to reach consensus has become a popular topic in linguistic group decision making (GDM). However, in linguistic contexts, it is commonly accepted that words mean different things for different people, which indicates the necessity of modeling experts’ personalized individual semantics (PISs). Moreover, experts sometimes may show non-cooperative behaviors during the consensus reaching process (CRP) due to their own interests. As a result, this paper focuses on developing a consensus reaching algorithm with non-cooperative behavior management for PIS-based social network GDM problems. First, linguistic preference relations are transformed into fuzzy preference relations by the PIS model, and then social network analysis techniques are used to obtain experts’ weight vector. Afterwards, we propose a feedback adjustment mechanism to improve experts’ adjustment willingness in CPRs, in which the trust relationships and the PISs of experts are utilized to generate adjustment advice for experts. Furthermore, a non-cooperative behavior management mechanism which dynamically adjusts the trust degrees in social network is devised. Followed by this, a numerical example is provided to demonstrate the proposed algorithm. Finally, detailed simulation results are presented to analyze the influence of different parameters on CRPs and illustrate the validity of the proposed algorithm.

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allfields	10.1080/01605682.2021.1997654 doi (DE-627)1833712080 (DE-599)KXP1833712080 DE-627 ger DE-627 rda eng Gao, Yuan verfasserin (DE-588)124151013X (DE-627)1771081139 aut Consensus reaching with non-cooperative behavior management for personalized individual semantics-based social network group decision making Yuan Gao and Zhen Zhang 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier social network analysis (dpeaa)DE-206 group decision making (dpeaa)DE-206 consensus reaching (dpeaa)DE-206 non-cooperative behavior (dpeaa)DE-206 personalized individual semantics (dpeaa)DE-206 Zhang, Zhen verfasserin (DE-588)1159576106 (DE-627)1022252089 (DE-576)505040034 aut Enthalten in Operational Research Society Journal of the Operational Research Society London : Taylor and Francis, 1978 73(2022), 11, Seite 2518-2535 Online-Ressource (DE-627)320465098 (DE-600)2007775-0 (DE-576)103939180 1476-9360 nnns volume:73 year:2022 number:11 pages:2518-2535 https://www.tandfonline.com/doi/pdf/10.1080/01605682.2021.1997654 Verlag lizenzpflichtig https://doi.org/10.1080/01605682.2021.1997654 Resolving-System lizenzpflichtig https://www.tandfonline.com/doi/epub/10.1080/01605682.2021.1997654 Verlag lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_374 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2107 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2935 GBV_ILN_2940 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 73 2022 11 2518-2535 26 01 0206 4268637443 x1z 08-02-23 26 00 DE-206 Leveraging social network trust relationships among experts to reach consensus has become a popular topic in linguistic group decision making (GDM). However, in linguistic contexts, it is commonly accepted that words mean different things for different people, which indicates the necessity of modeling experts’ personalized individual semantics (PISs). Moreover, experts sometimes may show non-cooperative behaviors during the consensus reaching process (CRP) due to their own interests. As a result, this paper focuses on developing a consensus reaching algorithm with non-cooperative behavior management for PIS-based social network GDM problems. First, linguistic preference relations are transformed into fuzzy preference relations by the PIS model, and then social network analysis techniques are used to obtain experts’ weight vector. Afterwards, we propose a feedback adjustment mechanism to improve experts’ adjustment willingness in CPRs, in which the trust relationships and the PISs of experts are utilized to generate adjustment advice for experts. Furthermore, a non-cooperative behavior management mechanism which dynamically adjusts the trust degrees in social network is devised. Followed by this, a numerical example is provided to demonstrate the proposed algorithm. Finally, detailed simulation results are presented to analyze the influence of different parameters on CRPs and illustrate the validity of the proposed algorithm.
spelling	10.1080/01605682.2021.1997654 doi (DE-627)1833712080 (DE-599)KXP1833712080 DE-627 ger DE-627 rda eng Gao, Yuan verfasserin (DE-588)124151013X (DE-627)1771081139 aut Consensus reaching with non-cooperative behavior management for personalized individual semantics-based social network group decision making Yuan Gao and Zhen Zhang 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier social network analysis (dpeaa)DE-206 group decision making (dpeaa)DE-206 consensus reaching (dpeaa)DE-206 non-cooperative behavior (dpeaa)DE-206 personalized individual semantics (dpeaa)DE-206 Zhang, Zhen verfasserin (DE-588)1159576106 (DE-627)1022252089 (DE-576)505040034 aut Enthalten in Operational Research Society Journal of the Operational Research Society London : Taylor and Francis, 1978 73(2022), 11, Seite 2518-2535 Online-Ressource (DE-627)320465098 (DE-600)2007775-0 (DE-576)103939180 1476-9360 nnns volume:73 year:2022 number:11 pages:2518-2535 https://www.tandfonline.com/doi/pdf/10.1080/01605682.2021.1997654 Verlag lizenzpflichtig https://doi.org/10.1080/01605682.2021.1997654 Resolving-System lizenzpflichtig https://www.tandfonline.com/doi/epub/10.1080/01605682.2021.1997654 Verlag lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_374 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2107 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2935 GBV_ILN_2940 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 73 2022 11 2518-2535 26 01 0206 4268637443 x1z 08-02-23 26 00 DE-206 Leveraging social network trust relationships among experts to reach consensus has become a popular topic in linguistic group decision making (GDM). However, in linguistic contexts, it is commonly accepted that words mean different things for different people, which indicates the necessity of modeling experts’ personalized individual semantics (PISs). Moreover, experts sometimes may show non-cooperative behaviors during the consensus reaching process (CRP) due to their own interests. As a result, this paper focuses on developing a consensus reaching algorithm with non-cooperative behavior management for PIS-based social network GDM problems. First, linguistic preference relations are transformed into fuzzy preference relations by the PIS model, and then social network analysis techniques are used to obtain experts’ weight vector. Afterwards, we propose a feedback adjustment mechanism to improve experts’ adjustment willingness in CPRs, in which the trust relationships and the PISs of experts are utilized to generate adjustment advice for experts. Furthermore, a non-cooperative behavior management mechanism which dynamically adjusts the trust degrees in social network is devised. Followed by this, a numerical example is provided to demonstrate the proposed algorithm. Finally, detailed simulation results are presented to analyze the influence of different parameters on CRPs and illustrate the validity of the proposed algorithm.
allfields_unstemmed	10.1080/01605682.2021.1997654 doi (DE-627)1833712080 (DE-599)KXP1833712080 DE-627 ger DE-627 rda eng Gao, Yuan verfasserin (DE-588)124151013X (DE-627)1771081139 aut Consensus reaching with non-cooperative behavior management for personalized individual semantics-based social network group decision making Yuan Gao and Zhen Zhang 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier social network analysis (dpeaa)DE-206 group decision making (dpeaa)DE-206 consensus reaching (dpeaa)DE-206 non-cooperative behavior (dpeaa)DE-206 personalized individual semantics (dpeaa)DE-206 Zhang, Zhen verfasserin (DE-588)1159576106 (DE-627)1022252089 (DE-576)505040034 aut Enthalten in Operational Research Society Journal of the Operational Research Society London : Taylor and Francis, 1978 73(2022), 11, Seite 2518-2535 Online-Ressource (DE-627)320465098 (DE-600)2007775-0 (DE-576)103939180 1476-9360 nnns volume:73 year:2022 number:11 pages:2518-2535 https://www.tandfonline.com/doi/pdf/10.1080/01605682.2021.1997654 Verlag lizenzpflichtig https://doi.org/10.1080/01605682.2021.1997654 Resolving-System lizenzpflichtig https://www.tandfonline.com/doi/epub/10.1080/01605682.2021.1997654 Verlag lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_374 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2107 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2935 GBV_ILN_2940 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 73 2022 11 2518-2535 26 01 0206 4268637443 x1z 08-02-23 26 00 DE-206 Leveraging social network trust relationships among experts to reach consensus has become a popular topic in linguistic group decision making (GDM). However, in linguistic contexts, it is commonly accepted that words mean different things for different people, which indicates the necessity of modeling experts’ personalized individual semantics (PISs). Moreover, experts sometimes may show non-cooperative behaviors during the consensus reaching process (CRP) due to their own interests. As a result, this paper focuses on developing a consensus reaching algorithm with non-cooperative behavior management for PIS-based social network GDM problems. First, linguistic preference relations are transformed into fuzzy preference relations by the PIS model, and then social network analysis techniques are used to obtain experts’ weight vector. Afterwards, we propose a feedback adjustment mechanism to improve experts’ adjustment willingness in CPRs, in which the trust relationships and the PISs of experts are utilized to generate adjustment advice for experts. Furthermore, a non-cooperative behavior management mechanism which dynamically adjusts the trust degrees in social network is devised. Followed by this, a numerical example is provided to demonstrate the proposed algorithm. Finally, detailed simulation results are presented to analyze the influence of different parameters on CRPs and illustrate the validity of the proposed algorithm.
allfieldsGer	10.1080/01605682.2021.1997654 doi (DE-627)1833712080 (DE-599)KXP1833712080 DE-627 ger DE-627 rda eng Gao, Yuan verfasserin (DE-588)124151013X (DE-627)1771081139 aut Consensus reaching with non-cooperative behavior management for personalized individual semantics-based social network group decision making Yuan Gao and Zhen Zhang 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier social network analysis (dpeaa)DE-206 group decision making (dpeaa)DE-206 consensus reaching (dpeaa)DE-206 non-cooperative behavior (dpeaa)DE-206 personalized individual semantics (dpeaa)DE-206 Zhang, Zhen verfasserin (DE-588)1159576106 (DE-627)1022252089 (DE-576)505040034 aut Enthalten in Operational Research Society Journal of the Operational Research Society London : Taylor and Francis, 1978 73(2022), 11, Seite 2518-2535 Online-Ressource (DE-627)320465098 (DE-600)2007775-0 (DE-576)103939180 1476-9360 nnns volume:73 year:2022 number:11 pages:2518-2535 https://www.tandfonline.com/doi/pdf/10.1080/01605682.2021.1997654 Verlag lizenzpflichtig https://doi.org/10.1080/01605682.2021.1997654 Resolving-System lizenzpflichtig https://www.tandfonline.com/doi/epub/10.1080/01605682.2021.1997654 Verlag lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_374 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2107 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2935 GBV_ILN_2940 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 73 2022 11 2518-2535 26 01 0206 4268637443 x1z 08-02-23 26 00 DE-206 Leveraging social network trust relationships among experts to reach consensus has become a popular topic in linguistic group decision making (GDM). However, in linguistic contexts, it is commonly accepted that words mean different things for different people, which indicates the necessity of modeling experts’ personalized individual semantics (PISs). Moreover, experts sometimes may show non-cooperative behaviors during the consensus reaching process (CRP) due to their own interests. As a result, this paper focuses on developing a consensus reaching algorithm with non-cooperative behavior management for PIS-based social network GDM problems. First, linguistic preference relations are transformed into fuzzy preference relations by the PIS model, and then social network analysis techniques are used to obtain experts’ weight vector. Afterwards, we propose a feedback adjustment mechanism to improve experts’ adjustment willingness in CPRs, in which the trust relationships and the PISs of experts are utilized to generate adjustment advice for experts. Furthermore, a non-cooperative behavior management mechanism which dynamically adjusts the trust degrees in social network is devised. Followed by this, a numerical example is provided to demonstrate the proposed algorithm. Finally, detailed simulation results are presented to analyze the influence of different parameters on CRPs and illustrate the validity of the proposed algorithm.
allfieldsSound	10.1080/01605682.2021.1997654 doi (DE-627)1833712080 (DE-599)KXP1833712080 DE-627 ger DE-627 rda eng Gao, Yuan verfasserin (DE-588)124151013X (DE-627)1771081139 aut Consensus reaching with non-cooperative behavior management for personalized individual semantics-based social network group decision making Yuan Gao and Zhen Zhang 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier social network analysis (dpeaa)DE-206 group decision making (dpeaa)DE-206 consensus reaching (dpeaa)DE-206 non-cooperative behavior (dpeaa)DE-206 personalized individual semantics (dpeaa)DE-206 Zhang, Zhen verfasserin (DE-588)1159576106 (DE-627)1022252089 (DE-576)505040034 aut Enthalten in Operational Research Society Journal of the Operational Research Society London : Taylor and Francis, 1978 73(2022), 11, Seite 2518-2535 Online-Ressource (DE-627)320465098 (DE-600)2007775-0 (DE-576)103939180 1476-9360 nnns volume:73 year:2022 number:11 pages:2518-2535 https://www.tandfonline.com/doi/pdf/10.1080/01605682.2021.1997654 Verlag lizenzpflichtig https://doi.org/10.1080/01605682.2021.1997654 Resolving-System lizenzpflichtig https://www.tandfonline.com/doi/epub/10.1080/01605682.2021.1997654 Verlag lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_63 GBV_ILN_70 GBV_ILN_100 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_374 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2107 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2935 GBV_ILN_2940 GBV_ILN_2949 GBV_ILN_2950 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 73 2022 11 2518-2535 26 01 0206 4268637443 x1z 08-02-23 26 00 DE-206 Leveraging social network trust relationships among experts to reach consensus has become a popular topic in linguistic group decision making (GDM). However, in linguistic contexts, it is commonly accepted that words mean different things for different people, which indicates the necessity of modeling experts’ personalized individual semantics (PISs). Moreover, experts sometimes may show non-cooperative behaviors during the consensus reaching process (CRP) due to their own interests. As a result, this paper focuses on developing a consensus reaching algorithm with non-cooperative behavior management for PIS-based social network GDM problems. First, linguistic preference relations are transformed into fuzzy preference relations by the PIS model, and then social network analysis techniques are used to obtain experts’ weight vector. Afterwards, we propose a feedback adjustment mechanism to improve experts’ adjustment willingness in CPRs, in which the trust relationships and the PISs of experts are utilized to generate adjustment advice for experts. Furthermore, a non-cooperative behavior management mechanism which dynamically adjusts the trust degrees in social network is devised. Followed by this, a numerical example is provided to demonstrate the proposed algorithm. Finally, detailed simulation results are presented to analyze the influence of different parameters on CRPs and illustrate the validity of the proposed algorithm.
language	English
source	Enthalten in Journal of the Operational Research Society 73(2022), 11, Seite 2518-2535 volume:73 year:2022 number:11 pages:2518-2535
sourceStr	Enthalten in Journal of the Operational Research Society 73(2022), 11, Seite 2518-2535 volume:73 year:2022 number:11 pages:2518-2535
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topic_facet	social network analysis group decision making consensus reaching non-cooperative behavior personalized individual semantics
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container_title	Journal of the Operational Research Society
authorswithroles_txt_mv	Gao, Yuan @@aut@@ Zhang, Zhen @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
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ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4346</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">73</subfield><subfield code="j">2022</subfield><subfield code="e">11</subfield><subfield code="h">2518-2535</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4268637443</subfield><subfield code="y">x1z</subfield><subfield code="z">08-02-23</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">Leveraging social network trust relationships among experts to reach consensus has become a popular topic in linguistic group decision making (GDM). However, in linguistic contexts, it is commonly accepted that words mean different things for different people, which indicates the necessity of modeling experts’ personalized individual semantics (PISs). Moreover, experts sometimes may show non-cooperative behaviors during the consensus reaching process (CRP) due to their own interests. As a result, this paper focuses on developing a consensus reaching algorithm with non-cooperative behavior management for PIS-based social network GDM problems. First, linguistic preference relations are transformed into fuzzy preference relations by the PIS model, and then social network analysis techniques are used to obtain experts’ weight vector. Afterwards, we propose a feedback adjustment mechanism to improve experts’ adjustment willingness in CPRs, in which the trust relationships and the PISs of experts are utilized to generate adjustment advice for experts. Furthermore, a non-cooperative behavior management mechanism which dynamically adjusts the trust degrees in social network is devised. Followed by this, a numerical example is provided to demonstrate the proposed algorithm. Finally, detailed simulation results are presented to analyze the influence of different parameters on CRPs and illustrate the validity of the proposed algorithm.</subfield></datafield></record></collection>
author	Gao, Yuan
spellingShingle	Gao, Yuan misc social network analysis misc group decision making misc consensus reaching misc non-cooperative behavior misc personalized individual semantics Consensus reaching with non-cooperative behavior management for personalized individual semantics-based social network group decision making
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topic_title	26 00 DE-206 Leveraging social network trust relationships among experts to reach consensus has become a popular topic in linguistic group decision making (GDM). However, in linguistic contexts, it is commonly accepted that words mean different things for different people, which indicates the necessity of modeling experts’ personalized individual semantics (PISs). Moreover, experts sometimes may show non-cooperative behaviors during the consensus reaching process (CRP) due to their own interests. As a result, this paper focuses on developing a consensus reaching algorithm with non-cooperative behavior management for PIS-based social network GDM problems. First, linguistic preference relations are transformed into fuzzy preference relations by the PIS model, and then social network analysis techniques are used to obtain experts’ weight vector. Afterwards, we propose a feedback adjustment mechanism to improve experts’ adjustment willingness in CPRs, in which the trust relationships and the PISs of experts are utilized to generate adjustment advice for experts. Furthermore, a non-cooperative behavior management mechanism which dynamically adjusts the trust degrees in social network is devised. Followed by this, a numerical example is provided to demonstrate the proposed algorithm. Finally, detailed simulation results are presented to analyze the influence of different parameters on CRPs and illustrate the validity of the proposed algorithm Consensus reaching with non-cooperative behavior management for personalized individual semantics-based social network group decision making Yuan Gao and Zhen Zhang social network analysis (dpeaa)DE-206 group decision making (dpeaa)DE-206 consensus reaching (dpeaa)DE-206 non-cooperative behavior (dpeaa)DE-206 personalized individual semantics (dpeaa)DE-206
topic	misc social network analysis misc group decision making misc consensus reaching misc non-cooperative behavior misc personalized individual semantics
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title_full	Consensus reaching with non-cooperative behavior management for personalized individual semantics-based social network group decision making Yuan Gao and Zhen Zhang
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title_short	Consensus reaching with non-cooperative behavior management for personalized individual semantics-based social network group decision making
url	https://www.tandfonline.com/doi/pdf/10.1080/01605682.2021.1997654 https://doi.org/10.1080/01605682.2021.1997654 https://www.tandfonline.com/doi/epub/10.1080/01605682.2021.1997654
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code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4268637443</subfield><subfield code="y">x1z</subfield><subfield code="z">08-02-23</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">Leveraging social network trust relationships among experts to reach consensus has become a popular topic in linguistic group decision making (GDM). However, in linguistic contexts, it is commonly accepted that words mean different things for different people, which indicates the necessity of modeling experts’ personalized individual semantics (PISs). Moreover, experts sometimes may show non-cooperative behaviors during the consensus reaching process (CRP) due to their own interests. As a result, this paper focuses on developing a consensus reaching algorithm with non-cooperative behavior management for PIS-based social network GDM problems. First, linguistic preference relations are transformed into fuzzy preference relations by the PIS model, and then social network analysis techniques are used to obtain experts’ weight vector. Afterwards, we propose a feedback adjustment mechanism to improve experts’ adjustment willingness in CPRs, in which the trust relationships and the PISs of experts are utilized to generate adjustment advice for experts. Furthermore, a non-cooperative behavior management mechanism which dynamically adjusts the trust degrees in social network is devised. Followed by this, a numerical example is provided to demonstrate the proposed algorithm. Finally, detailed simulation results are presented to analyze the influence of different parameters on CRPs and illustrate the validity of the proposed algorithm.</subfield></datafield></record></collection>
score	7.4014635

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Consensus reaching with non-cooperative behavior management for personalized individual semantics-based social network group decision making

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