Managing multi-granular probabilistic linguistic information in large-scale group decision making: A personalized individual semantics-based consensus model

The multi-granular probabilistic linguistic modeling allows decision makers to express cognitive information using multiple linguistic term sets based on their preferences. However, personalized individual semantics (PIS) can lead to different meanings of the same word within the linguistic context....
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Liu, Yuanyuan [verfasserIn] Yang, Youlong [verfasserIn] Sun, Liqin [verfasserIn] Huang, An [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Probabilistic linguistic preference relation Consensus reaching process Large-scale group decision making Multi-granular probabilistic linguistic information Personalized individual semantics

Übergeordnetes Werk:	Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 230
Übergeordnetes Werk:	volume:230

DOI / URN:	10.1016/j.eswa.2023.120645

Katalog-ID:	ELV010519629

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520			\|a The multi-granular probabilistic linguistic modeling allows decision makers to express cognitive information using multiple linguistic term sets based on their preferences. However, personalized individual semantics (PIS) can lead to different meanings of the same word within the linguistic context. To address this issue and manage consensus in large-scale group decision making, this study proposes a decision framework that employs multi-granular probabilistic linguistic preference relations (MGPLPRs). First, a transformation method is presented to unify different granularity levels of MGPLPRs, thus ensuring the consistency of granularity. Moreover, a consistency-driven optimization model is constructed to generate the numerical scales with PIS for different experts. Thereafter, a two-stage consensus reaching process (CRP) is developed, including both within-cluster and across-cluster CRP, to achieve group consensus. The experts’ original weights are derived from a social network, taking into account the trust relationships among them. A dynamic weighting mechanism is used to update the experts’ weights based on their contributions to group consensus, which better reflects the actual situation than fixed weights. The proposed method is exemplified through a case study of assessing and selecting campus surveillance measures for COVID-19. Finally, the effectiveness and robustness of the proposed framework are verified through comparative analysis and sensitivity analysis.
650		4	\|a Probabilistic linguistic preference relation
650		4	\|a Consensus reaching process
650		4	\|a Large-scale group decision making
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650		4	\|a Personalized individual semantics
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700	1		\|a Sun, Liqin \|e verfasserin \|4 aut
700	1		\|a Huang, An \|e verfasserin \|4 aut
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allfields	10.1016/j.eswa.2023.120645 doi (DE-627)ELV010519629 (ELSEVIER)S0957-4174(23)01147-8 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Liu, Yuanyuan verfasserin (orcid)0000-0002-7810-5473 aut Managing multi-granular probabilistic linguistic information in large-scale group decision making: A personalized individual semantics-based consensus model 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The multi-granular probabilistic linguistic modeling allows decision makers to express cognitive information using multiple linguistic term sets based on their preferences. However, personalized individual semantics (PIS) can lead to different meanings of the same word within the linguistic context. To address this issue and manage consensus in large-scale group decision making, this study proposes a decision framework that employs multi-granular probabilistic linguistic preference relations (MGPLPRs). First, a transformation method is presented to unify different granularity levels of MGPLPRs, thus ensuring the consistency of granularity. Moreover, a consistency-driven optimization model is constructed to generate the numerical scales with PIS for different experts. Thereafter, a two-stage consensus reaching process (CRP) is developed, including both within-cluster and across-cluster CRP, to achieve group consensus. The experts’ original weights are derived from a social network, taking into account the trust relationships among them. A dynamic weighting mechanism is used to update the experts’ weights based on their contributions to group consensus, which better reflects the actual situation than fixed weights. The proposed method is exemplified through a case study of assessing and selecting campus surveillance measures for COVID-19. Finally, the effectiveness and robustness of the proposed framework are verified through comparative analysis and sensitivity analysis. Probabilistic linguistic preference relation Consensus reaching process Large-scale group decision making Multi-granular probabilistic linguistic information Personalized individual semantics Yang, Youlong verfasserin (orcid)0000-0002-3706-1312 aut Sun, Liqin verfasserin aut Huang, An verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 230 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:230 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 230
spelling	10.1016/j.eswa.2023.120645 doi (DE-627)ELV010519629 (ELSEVIER)S0957-4174(23)01147-8 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Liu, Yuanyuan verfasserin (orcid)0000-0002-7810-5473 aut Managing multi-granular probabilistic linguistic information in large-scale group decision making: A personalized individual semantics-based consensus model 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The multi-granular probabilistic linguistic modeling allows decision makers to express cognitive information using multiple linguistic term sets based on their preferences. However, personalized individual semantics (PIS) can lead to different meanings of the same word within the linguistic context. To address this issue and manage consensus in large-scale group decision making, this study proposes a decision framework that employs multi-granular probabilistic linguistic preference relations (MGPLPRs). First, a transformation method is presented to unify different granularity levels of MGPLPRs, thus ensuring the consistency of granularity. Moreover, a consistency-driven optimization model is constructed to generate the numerical scales with PIS for different experts. Thereafter, a two-stage consensus reaching process (CRP) is developed, including both within-cluster and across-cluster CRP, to achieve group consensus. The experts’ original weights are derived from a social network, taking into account the trust relationships among them. A dynamic weighting mechanism is used to update the experts’ weights based on their contributions to group consensus, which better reflects the actual situation than fixed weights. The proposed method is exemplified through a case study of assessing and selecting campus surveillance measures for COVID-19. Finally, the effectiveness and robustness of the proposed framework are verified through comparative analysis and sensitivity analysis. Probabilistic linguistic preference relation Consensus reaching process Large-scale group decision making Multi-granular probabilistic linguistic information Personalized individual semantics Yang, Youlong verfasserin (orcid)0000-0002-3706-1312 aut Sun, Liqin verfasserin aut Huang, An verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 230 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:230 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 230
allfields_unstemmed	10.1016/j.eswa.2023.120645 doi (DE-627)ELV010519629 (ELSEVIER)S0957-4174(23)01147-8 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Liu, Yuanyuan verfasserin (orcid)0000-0002-7810-5473 aut Managing multi-granular probabilistic linguistic information in large-scale group decision making: A personalized individual semantics-based consensus model 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The multi-granular probabilistic linguistic modeling allows decision makers to express cognitive information using multiple linguistic term sets based on their preferences. However, personalized individual semantics (PIS) can lead to different meanings of the same word within the linguistic context. To address this issue and manage consensus in large-scale group decision making, this study proposes a decision framework that employs multi-granular probabilistic linguistic preference relations (MGPLPRs). First, a transformation method is presented to unify different granularity levels of MGPLPRs, thus ensuring the consistency of granularity. Moreover, a consistency-driven optimization model is constructed to generate the numerical scales with PIS for different experts. Thereafter, a two-stage consensus reaching process (CRP) is developed, including both within-cluster and across-cluster CRP, to achieve group consensus. The experts’ original weights are derived from a social network, taking into account the trust relationships among them. A dynamic weighting mechanism is used to update the experts’ weights based on their contributions to group consensus, which better reflects the actual situation than fixed weights. The proposed method is exemplified through a case study of assessing and selecting campus surveillance measures for COVID-19. Finally, the effectiveness and robustness of the proposed framework are verified through comparative analysis and sensitivity analysis. Probabilistic linguistic preference relation Consensus reaching process Large-scale group decision making Multi-granular probabilistic linguistic information Personalized individual semantics Yang, Youlong verfasserin (orcid)0000-0002-3706-1312 aut Sun, Liqin verfasserin aut Huang, An verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 230 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:230 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 230
allfieldsGer	10.1016/j.eswa.2023.120645 doi (DE-627)ELV010519629 (ELSEVIER)S0957-4174(23)01147-8 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Liu, Yuanyuan verfasserin (orcid)0000-0002-7810-5473 aut Managing multi-granular probabilistic linguistic information in large-scale group decision making: A personalized individual semantics-based consensus model 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The multi-granular probabilistic linguistic modeling allows decision makers to express cognitive information using multiple linguistic term sets based on their preferences. However, personalized individual semantics (PIS) can lead to different meanings of the same word within the linguistic context. To address this issue and manage consensus in large-scale group decision making, this study proposes a decision framework that employs multi-granular probabilistic linguistic preference relations (MGPLPRs). First, a transformation method is presented to unify different granularity levels of MGPLPRs, thus ensuring the consistency of granularity. Moreover, a consistency-driven optimization model is constructed to generate the numerical scales with PIS for different experts. Thereafter, a two-stage consensus reaching process (CRP) is developed, including both within-cluster and across-cluster CRP, to achieve group consensus. The experts’ original weights are derived from a social network, taking into account the trust relationships among them. A dynamic weighting mechanism is used to update the experts’ weights based on their contributions to group consensus, which better reflects the actual situation than fixed weights. The proposed method is exemplified through a case study of assessing and selecting campus surveillance measures for COVID-19. Finally, the effectiveness and robustness of the proposed framework are verified through comparative analysis and sensitivity analysis. Probabilistic linguistic preference relation Consensus reaching process Large-scale group decision making Multi-granular probabilistic linguistic information Personalized individual semantics Yang, Youlong verfasserin (orcid)0000-0002-3706-1312 aut Sun, Liqin verfasserin aut Huang, An verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 230 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:230 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 230
allfieldsSound	10.1016/j.eswa.2023.120645 doi (DE-627)ELV010519629 (ELSEVIER)S0957-4174(23)01147-8 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Liu, Yuanyuan verfasserin (orcid)0000-0002-7810-5473 aut Managing multi-granular probabilistic linguistic information in large-scale group decision making: A personalized individual semantics-based consensus model 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The multi-granular probabilistic linguistic modeling allows decision makers to express cognitive information using multiple linguistic term sets based on their preferences. However, personalized individual semantics (PIS) can lead to different meanings of the same word within the linguistic context. To address this issue and manage consensus in large-scale group decision making, this study proposes a decision framework that employs multi-granular probabilistic linguistic preference relations (MGPLPRs). First, a transformation method is presented to unify different granularity levels of MGPLPRs, thus ensuring the consistency of granularity. Moreover, a consistency-driven optimization model is constructed to generate the numerical scales with PIS for different experts. Thereafter, a two-stage consensus reaching process (CRP) is developed, including both within-cluster and across-cluster CRP, to achieve group consensus. The experts’ original weights are derived from a social network, taking into account the trust relationships among them. A dynamic weighting mechanism is used to update the experts’ weights based on their contributions to group consensus, which better reflects the actual situation than fixed weights. The proposed method is exemplified through a case study of assessing and selecting campus surveillance measures for COVID-19. Finally, the effectiveness and robustness of the proposed framework are verified through comparative analysis and sensitivity analysis. Probabilistic linguistic preference relation Consensus reaching process Large-scale group decision making Multi-granular probabilistic linguistic information Personalized individual semantics Yang, Youlong verfasserin (orcid)0000-0002-3706-1312 aut Sun, Liqin verfasserin aut Huang, An verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 230 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:230 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 230
language	English
source	Enthalten in Expert systems with applications 230 volume:230
sourceStr	Enthalten in Expert systems with applications 230 volume:230
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Probabilistic linguistic preference relation Consensus reaching process Large-scale group decision making Multi-granular probabilistic linguistic information Personalized individual semantics
dewey-raw	004
isfreeaccess_bool	false
container_title	Expert systems with applications
authorswithroles_txt_mv	Liu, Yuanyuan @@aut@@ Yang, Youlong @@aut@@ Sun, Liqin @@aut@@ Huang, An @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320577961
dewey-sort	14
id	ELV010519629
language_de	englisch
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author	Liu, Yuanyuan
spellingShingle	Liu, Yuanyuan ddc 004 bkl 54.72 misc Probabilistic linguistic preference relation misc Consensus reaching process misc Large-scale group decision making misc Multi-granular probabilistic linguistic information misc Personalized individual semantics Managing multi-granular probabilistic linguistic information in large-scale group decision making: A personalized individual semantics-based consensus model
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topic_title	004 VZ 54.72 bkl Managing multi-granular probabilistic linguistic information in large-scale group decision making: A personalized individual semantics-based consensus model Probabilistic linguistic preference relation Consensus reaching process Large-scale group decision making Multi-granular probabilistic linguistic information Personalized individual semantics
topic	ddc 004 bkl 54.72 misc Probabilistic linguistic preference relation misc Consensus reaching process misc Large-scale group decision making misc Multi-granular probabilistic linguistic information misc Personalized individual semantics
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title	Managing multi-granular probabilistic linguistic information in large-scale group decision making: A personalized individual semantics-based consensus model
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title_full	Managing multi-granular probabilistic linguistic information in large-scale group decision making: A personalized individual semantics-based consensus model
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title_sort	managing multi-granular probabilistic linguistic information in large-scale group decision making: a personalized individual semantics-based consensus model
title_auth	Managing multi-granular probabilistic linguistic information in large-scale group decision making: A personalized individual semantics-based consensus model
abstract	The multi-granular probabilistic linguistic modeling allows decision makers to express cognitive information using multiple linguistic term sets based on their preferences. However, personalized individual semantics (PIS) can lead to different meanings of the same word within the linguistic context. To address this issue and manage consensus in large-scale group decision making, this study proposes a decision framework that employs multi-granular probabilistic linguistic preference relations (MGPLPRs). First, a transformation method is presented to unify different granularity levels of MGPLPRs, thus ensuring the consistency of granularity. Moreover, a consistency-driven optimization model is constructed to generate the numerical scales with PIS for different experts. Thereafter, a two-stage consensus reaching process (CRP) is developed, including both within-cluster and across-cluster CRP, to achieve group consensus. The experts’ original weights are derived from a social network, taking into account the trust relationships among them. A dynamic weighting mechanism is used to update the experts’ weights based on their contributions to group consensus, which better reflects the actual situation than fixed weights. The proposed method is exemplified through a case study of assessing and selecting campus surveillance measures for COVID-19. Finally, the effectiveness and robustness of the proposed framework are verified through comparative analysis and sensitivity analysis.
abstractGer	The multi-granular probabilistic linguistic modeling allows decision makers to express cognitive information using multiple linguistic term sets based on their preferences. However, personalized individual semantics (PIS) can lead to different meanings of the same word within the linguistic context. To address this issue and manage consensus in large-scale group decision making, this study proposes a decision framework that employs multi-granular probabilistic linguistic preference relations (MGPLPRs). First, a transformation method is presented to unify different granularity levels of MGPLPRs, thus ensuring the consistency of granularity. Moreover, a consistency-driven optimization model is constructed to generate the numerical scales with PIS for different experts. Thereafter, a two-stage consensus reaching process (CRP) is developed, including both within-cluster and across-cluster CRP, to achieve group consensus. The experts’ original weights are derived from a social network, taking into account the trust relationships among them. A dynamic weighting mechanism is used to update the experts’ weights based on their contributions to group consensus, which better reflects the actual situation than fixed weights. The proposed method is exemplified through a case study of assessing and selecting campus surveillance measures for COVID-19. Finally, the effectiveness and robustness of the proposed framework are verified through comparative analysis and sensitivity analysis.
abstract_unstemmed	The multi-granular probabilistic linguistic modeling allows decision makers to express cognitive information using multiple linguistic term sets based on their preferences. However, personalized individual semantics (PIS) can lead to different meanings of the same word within the linguistic context. To address this issue and manage consensus in large-scale group decision making, this study proposes a decision framework that employs multi-granular probabilistic linguistic preference relations (MGPLPRs). First, a transformation method is presented to unify different granularity levels of MGPLPRs, thus ensuring the consistency of granularity. Moreover, a consistency-driven optimization model is constructed to generate the numerical scales with PIS for different experts. Thereafter, a two-stage consensus reaching process (CRP) is developed, including both within-cluster and across-cluster CRP, to achieve group consensus. The experts’ original weights are derived from a social network, taking into account the trust relationships among them. A dynamic weighting mechanism is used to update the experts’ weights based on their contributions to group consensus, which better reflects the actual situation than fixed weights. The proposed method is exemplified through a case study of assessing and selecting campus surveillance measures for COVID-19. Finally, the effectiveness and robustness of the proposed framework are verified through comparative analysis and sensitivity analysis.
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title_short	Managing multi-granular probabilistic linguistic information in large-scale group decision making: A personalized individual semantics-based consensus model
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Managing multi-granular probabilistic linguistic information in large-scale group decision making: A personalized individual semantics-based consensus model

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