Managing Ignorance Elements and Personalized Individual Semantics Under Incomplete Linguistic Distribution Context in Group Decision Making
Abstract Linguistic distribution expressions provide a flexible way for decision makers to express their opinions in linguistic decision making. When working with a linguistic distribution, words mean different things for different people, i.e., decision makers have personalized individual semantics...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Cong-Cong [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Nature B.V. 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Group decision and negotiation - Springer Netherlands, 1992, 30(2020), 1 vom: 03. Okt., Seite 97-118 |
|---|---|
| Übergeordnetes Werk: |
volume:30 ; year:2020 ; number:1 ; day:03 ; month:10 ; pages:97-118 |
| Links: |
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| DOI / URN: |
10.1007/s10726-020-09708-9 |
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| 520 | |a Abstract Linguistic distribution expressions provide a flexible way for decision makers to express their opinions in linguistic decision making. When working with a linguistic distribution, words mean different things for different people, i.e., decision makers have personalized individual semantics (PISs) regarding words. Therefore, in this paper, we propose a consistency-driven methodology to manage distribution linguistic preference relations (DLPRs) with PISs. This methodology can not only estimate the ignorance elements in incomplete DLPRs but also obtain the personalized numerical meanings of linguistic expressions to decision makers. In this way, we can combine the characteristics of the personalized representation in linguistic decision making and guarantee the optimum consistency of incomplete DLPRs with ignorance elements. Detailed numerical and comparison analyses have been proposed to justify our proposal. | ||
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Abstract Linguistic distribution expressions provide a flexible way for decision makers to express their opinions in linguistic decision making. When working with a linguistic distribution, words mean different things for different people, i.e., decision makers have personalized individual semantics (PISs) regarding words. Therefore, in this paper, we propose a consistency-driven methodology to manage distribution linguistic preference relations (DLPRs) with PISs. This methodology can not only estimate the ignorance elements in incomplete DLPRs but also obtain the personalized numerical meanings of linguistic expressions to decision makers. In this way, we can combine the characteristics of the personalized representation in linguistic decision making and guarantee the optimum consistency of incomplete DLPRs with ignorance elements. Detailed numerical and comparison analyses have been proposed to justify our proposal. © Springer Nature B.V. 2020 |
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Abstract Linguistic distribution expressions provide a flexible way for decision makers to express their opinions in linguistic decision making. When working with a linguistic distribution, words mean different things for different people, i.e., decision makers have personalized individual semantics (PISs) regarding words. Therefore, in this paper, we propose a consistency-driven methodology to manage distribution linguistic preference relations (DLPRs) with PISs. This methodology can not only estimate the ignorance elements in incomplete DLPRs but also obtain the personalized numerical meanings of linguistic expressions to decision makers. In this way, we can combine the characteristics of the personalized representation in linguistic decision making and guarantee the optimum consistency of incomplete DLPRs with ignorance elements. Detailed numerical and comparison analyses have been proposed to justify our proposal. © Springer Nature B.V. 2020 |
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Abstract Linguistic distribution expressions provide a flexible way for decision makers to express their opinions in linguistic decision making. When working with a linguistic distribution, words mean different things for different people, i.e., decision makers have personalized individual semantics (PISs) regarding words. Therefore, in this paper, we propose a consistency-driven methodology to manage distribution linguistic preference relations (DLPRs) with PISs. This methodology can not only estimate the ignorance elements in incomplete DLPRs but also obtain the personalized numerical meanings of linguistic expressions to decision makers. In this way, we can combine the characteristics of the personalized representation in linguistic decision making and guarantee the optimum consistency of incomplete DLPRs with ignorance elements. Detailed numerical and comparison analyses have been proposed to justify our proposal. © Springer Nature B.V. 2020 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2123995215</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505082315.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230505s2020 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10726-020-09708-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2123995215</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10726-020-09708-9-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">150</subfield><subfield code="a">300</subfield><subfield code="a">650</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">5,2</subfield><subfield code="a">3,4</subfield><subfield code="a">3,2</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Cong-Cong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Managing Ignorance Elements and Personalized Individual Semantics Under Incomplete Linguistic Distribution Context in Group Decision Making</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Nature B.V. 2020</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Linguistic distribution expressions provide a flexible way for decision makers to express their opinions in linguistic decision making. When working with a linguistic distribution, words mean different things for different people, i.e., decision makers have personalized individual semantics (PISs) regarding words. Therefore, in this paper, we propose a consistency-driven methodology to manage distribution linguistic preference relations (DLPRs) with PISs. This methodology can not only estimate the ignorance elements in incomplete DLPRs but also obtain the personalized numerical meanings of linguistic expressions to decision makers. In this way, we can combine the characteristics of the personalized representation in linguistic decision making and guarantee the optimum consistency of incomplete DLPRs with ignorance elements. 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Okt., Seite 97-118</subfield><subfield code="w">(DE-627)17112684X</subfield><subfield code="w">(DE-600)1155213-X</subfield><subfield code="w">(DE-576)040094448</subfield><subfield code="x">0926-2644</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:30</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:1</subfield><subfield code="g">day:03</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:97-118</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10726-020-09708-9</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-WIW</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">30</subfield><subfield code="j">2020</subfield><subfield code="e">1</subfield><subfield code="b">03</subfield><subfield code="c">10</subfield><subfield code="h">97-118</subfield></datafield></record></collection>
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