Mining Personalized Individual Semantics of Self-confidence Participants in Linguistic Group Decision-Making

Abstract Words representing individual preferences in group decision-making (GDM) are always associated with different meanings. Consequently, mining personalized semantics of decision-makers (DMs) hidden in preference expressions, and establishing a corresponding management mechanism, is an effecti...
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Autor*in:	Jing, Limei [verfasserIn] Chao, Xiangrui

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Consensus process Linguistic group decision-making Personalized individual semantics Self-confidence

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: International journal of computational intelligence systems - Paris : Atlantis Press, 2008, 15(2022), 1 vom: 21. Sept.
Übergeordnetes Werk:	volume:15 ; year:2022 ; number:1 ; day:21 ; month:09

Links:	Volltext

DOI / URN:	10.1007/s44196-022-00136-y

Katalog-ID:	SPR048170364

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allfields	10.1007/s44196-022-00136-y doi (DE-627)SPR048170364 (SPR)s44196-022-00136-y-e DE-627 ger DE-627 rakwb eng Jing, Limei verfasserin aut Mining Personalized Individual Semantics of Self-confidence Participants in Linguistic Group Decision-Making 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Words representing individual preferences in group decision-making (GDM) are always associated with different meanings. Consequently, mining personalized semantics of decision-makers (DMs) hidden in preference expressions, and establishing a corresponding management mechanism, is an effective way to reach group consensus through computing with word methodology. However, the aforementioned consensus-reaching process may be hindered by self-confidence. To address this limitation, this study proposes a linguistic group decision model with self-confidence behavior. First, we identified the corresponding self-confidence levels for each DM. Next, we integrated different linguistic representation models into unified linguistic distribution-based models. We then obtained individual personalized semantics based on a consistency-driven optimization method, and designed a feedback-adjustment mechanism to improve the adjustment willingness of DMs and group consensus level. Finally, we conducted a quantitative experiment to demonstrate our model’s effectiveness and feasibility. Consensus process (dpeaa)DE-He213 Linguistic group decision-making (dpeaa)DE-He213 Personalized individual semantics (dpeaa)DE-He213 Self-confidence (dpeaa)DE-He213 Chao, Xiangrui aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 15(2022), 1 vom: 21. Sept. (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:15 year:2022 number:1 day:21 month:09 https://dx.doi.org/10.1007/s44196-022-00136-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 1 21 09
spelling	10.1007/s44196-022-00136-y doi (DE-627)SPR048170364 (SPR)s44196-022-00136-y-e DE-627 ger DE-627 rakwb eng Jing, Limei verfasserin aut Mining Personalized Individual Semantics of Self-confidence Participants in Linguistic Group Decision-Making 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Words representing individual preferences in group decision-making (GDM) are always associated with different meanings. Consequently, mining personalized semantics of decision-makers (DMs) hidden in preference expressions, and establishing a corresponding management mechanism, is an effective way to reach group consensus through computing with word methodology. However, the aforementioned consensus-reaching process may be hindered by self-confidence. To address this limitation, this study proposes a linguistic group decision model with self-confidence behavior. First, we identified the corresponding self-confidence levels for each DM. Next, we integrated different linguistic representation models into unified linguistic distribution-based models. We then obtained individual personalized semantics based on a consistency-driven optimization method, and designed a feedback-adjustment mechanism to improve the adjustment willingness of DMs and group consensus level. Finally, we conducted a quantitative experiment to demonstrate our model’s effectiveness and feasibility. Consensus process (dpeaa)DE-He213 Linguistic group decision-making (dpeaa)DE-He213 Personalized individual semantics (dpeaa)DE-He213 Self-confidence (dpeaa)DE-He213 Chao, Xiangrui aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 15(2022), 1 vom: 21. Sept. (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:15 year:2022 number:1 day:21 month:09 https://dx.doi.org/10.1007/s44196-022-00136-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 1 21 09
allfields_unstemmed	10.1007/s44196-022-00136-y doi (DE-627)SPR048170364 (SPR)s44196-022-00136-y-e DE-627 ger DE-627 rakwb eng Jing, Limei verfasserin aut Mining Personalized Individual Semantics of Self-confidence Participants in Linguistic Group Decision-Making 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Words representing individual preferences in group decision-making (GDM) are always associated with different meanings. Consequently, mining personalized semantics of decision-makers (DMs) hidden in preference expressions, and establishing a corresponding management mechanism, is an effective way to reach group consensus through computing with word methodology. However, the aforementioned consensus-reaching process may be hindered by self-confidence. To address this limitation, this study proposes a linguistic group decision model with self-confidence behavior. First, we identified the corresponding self-confidence levels for each DM. Next, we integrated different linguistic representation models into unified linguistic distribution-based models. We then obtained individual personalized semantics based on a consistency-driven optimization method, and designed a feedback-adjustment mechanism to improve the adjustment willingness of DMs and group consensus level. Finally, we conducted a quantitative experiment to demonstrate our model’s effectiveness and feasibility. Consensus process (dpeaa)DE-He213 Linguistic group decision-making (dpeaa)DE-He213 Personalized individual semantics (dpeaa)DE-He213 Self-confidence (dpeaa)DE-He213 Chao, Xiangrui aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 15(2022), 1 vom: 21. Sept. (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:15 year:2022 number:1 day:21 month:09 https://dx.doi.org/10.1007/s44196-022-00136-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 1 21 09
allfieldsGer	10.1007/s44196-022-00136-y doi (DE-627)SPR048170364 (SPR)s44196-022-00136-y-e DE-627 ger DE-627 rakwb eng Jing, Limei verfasserin aut Mining Personalized Individual Semantics of Self-confidence Participants in Linguistic Group Decision-Making 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Words representing individual preferences in group decision-making (GDM) are always associated with different meanings. Consequently, mining personalized semantics of decision-makers (DMs) hidden in preference expressions, and establishing a corresponding management mechanism, is an effective way to reach group consensus through computing with word methodology. However, the aforementioned consensus-reaching process may be hindered by self-confidence. To address this limitation, this study proposes a linguistic group decision model with self-confidence behavior. First, we identified the corresponding self-confidence levels for each DM. Next, we integrated different linguistic representation models into unified linguistic distribution-based models. We then obtained individual personalized semantics based on a consistency-driven optimization method, and designed a feedback-adjustment mechanism to improve the adjustment willingness of DMs and group consensus level. Finally, we conducted a quantitative experiment to demonstrate our model’s effectiveness and feasibility. Consensus process (dpeaa)DE-He213 Linguistic group decision-making (dpeaa)DE-He213 Personalized individual semantics (dpeaa)DE-He213 Self-confidence (dpeaa)DE-He213 Chao, Xiangrui aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 15(2022), 1 vom: 21. Sept. (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:15 year:2022 number:1 day:21 month:09 https://dx.doi.org/10.1007/s44196-022-00136-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 1 21 09
allfieldsSound	10.1007/s44196-022-00136-y doi (DE-627)SPR048170364 (SPR)s44196-022-00136-y-e DE-627 ger DE-627 rakwb eng Jing, Limei verfasserin aut Mining Personalized Individual Semantics of Self-confidence Participants in Linguistic Group Decision-Making 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Words representing individual preferences in group decision-making (GDM) are always associated with different meanings. Consequently, mining personalized semantics of decision-makers (DMs) hidden in preference expressions, and establishing a corresponding management mechanism, is an effective way to reach group consensus through computing with word methodology. However, the aforementioned consensus-reaching process may be hindered by self-confidence. To address this limitation, this study proposes a linguistic group decision model with self-confidence behavior. First, we identified the corresponding self-confidence levels for each DM. Next, we integrated different linguistic representation models into unified linguistic distribution-based models. We then obtained individual personalized semantics based on a consistency-driven optimization method, and designed a feedback-adjustment mechanism to improve the adjustment willingness of DMs and group consensus level. Finally, we conducted a quantitative experiment to demonstrate our model’s effectiveness and feasibility. Consensus process (dpeaa)DE-He213 Linguistic group decision-making (dpeaa)DE-He213 Personalized individual semantics (dpeaa)DE-He213 Self-confidence (dpeaa)DE-He213 Chao, Xiangrui aut Enthalten in International journal of computational intelligence systems Paris : Atlantis Press, 2008 15(2022), 1 vom: 21. Sept. (DE-627)777781514 (DE-600)2754752-8 1875-6883 nnns volume:15 year:2022 number:1 day:21 month:09 https://dx.doi.org/10.1007/s44196-022-00136-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 1 21 09
language	English
source	Enthalten in International journal of computational intelligence systems 15(2022), 1 vom: 21. Sept. volume:15 year:2022 number:1 day:21 month:09
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author	Jing, Limei
spellingShingle	Jing, Limei misc Consensus process misc Linguistic group decision-making misc Personalized individual semantics misc Self-confidence Mining Personalized Individual Semantics of Self-confidence Participants in Linguistic Group Decision-Making
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topic_title	Mining Personalized Individual Semantics of Self-confidence Participants in Linguistic Group Decision-Making Consensus process (dpeaa)DE-He213 Linguistic group decision-making (dpeaa)DE-He213 Personalized individual semantics (dpeaa)DE-He213 Self-confidence (dpeaa)DE-He213
topic	misc Consensus process misc Linguistic group decision-making misc Personalized individual semantics misc Self-confidence
topic_unstemmed	misc Consensus process misc Linguistic group decision-making misc Personalized individual semantics misc Self-confidence
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title	Mining Personalized Individual Semantics of Self-confidence Participants in Linguistic Group Decision-Making
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title_full	Mining Personalized Individual Semantics of Self-confidence Participants in Linguistic Group Decision-Making
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title_sort	mining personalized individual semantics of self-confidence participants in linguistic group decision-making
title_auth	Mining Personalized Individual Semantics of Self-confidence Participants in Linguistic Group Decision-Making
abstract	Abstract Words representing individual preferences in group decision-making (GDM) are always associated with different meanings. Consequently, mining personalized semantics of decision-makers (DMs) hidden in preference expressions, and establishing a corresponding management mechanism, is an effective way to reach group consensus through computing with word methodology. However, the aforementioned consensus-reaching process may be hindered by self-confidence. To address this limitation, this study proposes a linguistic group decision model with self-confidence behavior. First, we identified the corresponding self-confidence levels for each DM. Next, we integrated different linguistic representation models into unified linguistic distribution-based models. We then obtained individual personalized semantics based on a consistency-driven optimization method, and designed a feedback-adjustment mechanism to improve the adjustment willingness of DMs and group consensus level. Finally, we conducted a quantitative experiment to demonstrate our model’s effectiveness and feasibility. © The Author(s) 2022
abstractGer	Abstract Words representing individual preferences in group decision-making (GDM) are always associated with different meanings. Consequently, mining personalized semantics of decision-makers (DMs) hidden in preference expressions, and establishing a corresponding management mechanism, is an effective way to reach group consensus through computing with word methodology. However, the aforementioned consensus-reaching process may be hindered by self-confidence. To address this limitation, this study proposes a linguistic group decision model with self-confidence behavior. First, we identified the corresponding self-confidence levels for each DM. Next, we integrated different linguistic representation models into unified linguistic distribution-based models. We then obtained individual personalized semantics based on a consistency-driven optimization method, and designed a feedback-adjustment mechanism to improve the adjustment willingness of DMs and group consensus level. Finally, we conducted a quantitative experiment to demonstrate our model’s effectiveness and feasibility. © The Author(s) 2022
abstract_unstemmed	Abstract Words representing individual preferences in group decision-making (GDM) are always associated with different meanings. Consequently, mining personalized semantics of decision-makers (DMs) hidden in preference expressions, and establishing a corresponding management mechanism, is an effective way to reach group consensus through computing with word methodology. However, the aforementioned consensus-reaching process may be hindered by self-confidence. To address this limitation, this study proposes a linguistic group decision model with self-confidence behavior. First, we identified the corresponding self-confidence levels for each DM. Next, we integrated different linguistic representation models into unified linguistic distribution-based models. We then obtained individual personalized semantics based on a consistency-driven optimization method, and designed a feedback-adjustment mechanism to improve the adjustment willingness of DMs and group consensus level. Finally, we conducted a quantitative experiment to demonstrate our model’s effectiveness and feasibility. © The Author(s) 2022
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title_short	Mining Personalized Individual Semantics of Self-confidence Participants in Linguistic Group Decision-Making
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