How to choose a fuzzy similarity measure in decision-making?

Purpose - The paper is dedicated to the analysis of fuzzy similarity measures in uncertainty analysis in general, and in economic decision-making in particular. The purpose of this paper is to explain how a similarity measure can be chosen to quantify a qualitative description of similarities provid...
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Gespeichert in:

Autor*in:	Bouchon-Meunier, Bernadette - 1948- [verfasserIn] Coletti, Giulianella [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Rechteinformationen:	Open Access Namensnennung 4.0 International ; CC BY 4.0

Schlagwörter:	Fuzzy sets Similarity measures Dissimilarity measure Measurement theory,Qualitative choice

Übergeordnetes Werk:	Enthalten in: Asian journal of economics and banking - Bingley : Emerald Publishing Limited, 2020, 4(2020), 3, Seite 37-48
Übergeordnetes Werk:	volume:4 ; year:2020 ; number:3 ; pages:37-48

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1108/AJEB-08-2020-0055

Katalog-ID:	1758320001

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allfields	10.1108/AJEB-08-2020-0055 doi (DE-627)1758320001 (DE-599)KXP1758320001 DE-627 ger DE-627 rda eng C00 C18 C10 jelc Bouchon-Meunier, Bernadette 1948- verfasserin (DE-588)111566851 (DE-627)47100376X (DE-576)168268094 aut How to choose a fuzzy similarity measure in decision-making? Bernadette Bouchon-Meunier, Giulianella Coletti 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Purpose - The paper is dedicated to the analysis of fuzzy similarity measures in uncertainty analysis in general, and in economic decision-making in particular. The purpose of this paper is to explain how a similarity measure can be chosen to quantify a qualitative description of similarities provided by experts of a given domain, in the case where the objects to compare are described through imprecise or linguistic attribute values represented by fuzzy sets. The case of qualitative dissimilarities is also addressed and the particular case of their representation by distances is presented. Design/methodology/approach - The approach is based on measurement theory, following Tversky’s well-known paradigm. Findings - A list of axioms which may or may not be satisfied by a qualitative comparative similarity between fuzzy objects is proposed, as extensions of axioms satisfied by similarities between crisp objects. They enable to express necessary and sufficient conditions for a numerical similarity measure to represent a comparative similarity between fuzzy objects. The representation of comparative dissimilarities is also addressed by means of specific functions depending on the distance between attribute values. Originality/value - Examples of functions satisfying certain axioms to represent comparative similarities are given. They are based on the choice of operators to compute intersection, union and difference of fuzzy sets. A simple application of this methodology to economy is given, to show how a measure of similarity can be chosen to represent intuitive similarities expressed by an economist by means of a quantitative measure easily calculable. More detailed and formal results are given in Coletti and Bouchon-Meunier (2020) for similarities and Coletti et al. (2020) for dissimilarities. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/legalcode Fuzzy sets (dpeaa)DE-206 Similarity measures (dpeaa)DE-206 Dissimilarity measure (dpeaa)DE-206 Measurement theory,Qualitative choice (dpeaa)DE-206 Coletti, Giulianella verfasserin aut Enthalten in Asian journal of economics and banking Bingley : Emerald Publishing Limited, 2020 4(2020), 3, Seite 37-48 Online-Ressource (DE-627)1757052313 (DE-600)3062819-2 2615-9821 nnns volume:4 year:2020 number:3 pages:37-48 https://www.emerald.com/insight/content/doi/10.1108/AJEB-08-2020-0055/full/pdf?title=how-to-choose-a-fuzzy-similarity-measure-in-decision-making Verlag kostenfrei https://doi.org/10.1108/AJEB-08-2020-0055 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 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 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 4 2020 3 37-48 26 01 0206 3930079550 x1z 21-05-21 2403 01 DE-LFER 3936212252 00 --%%-- --%%-- n --%%-- l01 09-06-21 2403 01 DE-LFER https://doi.org/10.1108/AJEB-08-2020-0055 2403 01 DE-LFER https://www.emerald.com/insight/content/doi/10.1108/AJEB-08-2020-0055/full/pdf?title=how-to-choose-a-fuzzy-similarity-measure-in-decision-making
spelling	10.1108/AJEB-08-2020-0055 doi (DE-627)1758320001 (DE-599)KXP1758320001 DE-627 ger DE-627 rda eng C00 C18 C10 jelc Bouchon-Meunier, Bernadette 1948- verfasserin (DE-588)111566851 (DE-627)47100376X (DE-576)168268094 aut How to choose a fuzzy similarity measure in decision-making? Bernadette Bouchon-Meunier, Giulianella Coletti 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Purpose - The paper is dedicated to the analysis of fuzzy similarity measures in uncertainty analysis in general, and in economic decision-making in particular. The purpose of this paper is to explain how a similarity measure can be chosen to quantify a qualitative description of similarities provided by experts of a given domain, in the case where the objects to compare are described through imprecise or linguistic attribute values represented by fuzzy sets. The case of qualitative dissimilarities is also addressed and the particular case of their representation by distances is presented. Design/methodology/approach - The approach is based on measurement theory, following Tversky’s well-known paradigm. Findings - A list of axioms which may or may not be satisfied by a qualitative comparative similarity between fuzzy objects is proposed, as extensions of axioms satisfied by similarities between crisp objects. They enable to express necessary and sufficient conditions for a numerical similarity measure to represent a comparative similarity between fuzzy objects. The representation of comparative dissimilarities is also addressed by means of specific functions depending on the distance between attribute values. Originality/value - Examples of functions satisfying certain axioms to represent comparative similarities are given. They are based on the choice of operators to compute intersection, union and difference of fuzzy sets. A simple application of this methodology to economy is given, to show how a measure of similarity can be chosen to represent intuitive similarities expressed by an economist by means of a quantitative measure easily calculable. More detailed and formal results are given in Coletti and Bouchon-Meunier (2020) for similarities and Coletti et al. (2020) for dissimilarities. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/legalcode Fuzzy sets (dpeaa)DE-206 Similarity measures (dpeaa)DE-206 Dissimilarity measure (dpeaa)DE-206 Measurement theory,Qualitative choice (dpeaa)DE-206 Coletti, Giulianella verfasserin aut Enthalten in Asian journal of economics and banking Bingley : Emerald Publishing Limited, 2020 4(2020), 3, Seite 37-48 Online-Ressource (DE-627)1757052313 (DE-600)3062819-2 2615-9821 nnns volume:4 year:2020 number:3 pages:37-48 https://www.emerald.com/insight/content/doi/10.1108/AJEB-08-2020-0055/full/pdf?title=how-to-choose-a-fuzzy-similarity-measure-in-decision-making Verlag kostenfrei https://doi.org/10.1108/AJEB-08-2020-0055 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 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 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 4 2020 3 37-48 26 01 0206 3930079550 x1z 21-05-21 2403 01 DE-LFER 3936212252 00 --%%-- --%%-- n --%%-- l01 09-06-21 2403 01 DE-LFER https://doi.org/10.1108/AJEB-08-2020-0055 2403 01 DE-LFER https://www.emerald.com/insight/content/doi/10.1108/AJEB-08-2020-0055/full/pdf?title=how-to-choose-a-fuzzy-similarity-measure-in-decision-making
allfields_unstemmed	10.1108/AJEB-08-2020-0055 doi (DE-627)1758320001 (DE-599)KXP1758320001 DE-627 ger DE-627 rda eng C00 C18 C10 jelc Bouchon-Meunier, Bernadette 1948- verfasserin (DE-588)111566851 (DE-627)47100376X (DE-576)168268094 aut How to choose a fuzzy similarity measure in decision-making? Bernadette Bouchon-Meunier, Giulianella Coletti 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Purpose - The paper is dedicated to the analysis of fuzzy similarity measures in uncertainty analysis in general, and in economic decision-making in particular. The purpose of this paper is to explain how a similarity measure can be chosen to quantify a qualitative description of similarities provided by experts of a given domain, in the case where the objects to compare are described through imprecise or linguistic attribute values represented by fuzzy sets. The case of qualitative dissimilarities is also addressed and the particular case of their representation by distances is presented. Design/methodology/approach - The approach is based on measurement theory, following Tversky’s well-known paradigm. Findings - A list of axioms which may or may not be satisfied by a qualitative comparative similarity between fuzzy objects is proposed, as extensions of axioms satisfied by similarities between crisp objects. They enable to express necessary and sufficient conditions for a numerical similarity measure to represent a comparative similarity between fuzzy objects. The representation of comparative dissimilarities is also addressed by means of specific functions depending on the distance between attribute values. Originality/value - Examples of functions satisfying certain axioms to represent comparative similarities are given. They are based on the choice of operators to compute intersection, union and difference of fuzzy sets. A simple application of this methodology to economy is given, to show how a measure of similarity can be chosen to represent intuitive similarities expressed by an economist by means of a quantitative measure easily calculable. More detailed and formal results are given in Coletti and Bouchon-Meunier (2020) for similarities and Coletti et al. (2020) for dissimilarities. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/legalcode Fuzzy sets (dpeaa)DE-206 Similarity measures (dpeaa)DE-206 Dissimilarity measure (dpeaa)DE-206 Measurement theory,Qualitative choice (dpeaa)DE-206 Coletti, Giulianella verfasserin aut Enthalten in Asian journal of economics and banking Bingley : Emerald Publishing Limited, 2020 4(2020), 3, Seite 37-48 Online-Ressource (DE-627)1757052313 (DE-600)3062819-2 2615-9821 nnns volume:4 year:2020 number:3 pages:37-48 https://www.emerald.com/insight/content/doi/10.1108/AJEB-08-2020-0055/full/pdf?title=how-to-choose-a-fuzzy-similarity-measure-in-decision-making Verlag kostenfrei https://doi.org/10.1108/AJEB-08-2020-0055 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 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 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 4 2020 3 37-48 26 01 0206 3930079550 x1z 21-05-21 2403 01 DE-LFER 3936212252 00 --%%-- --%%-- n --%%-- l01 09-06-21 2403 01 DE-LFER https://doi.org/10.1108/AJEB-08-2020-0055 2403 01 DE-LFER https://www.emerald.com/insight/content/doi/10.1108/AJEB-08-2020-0055/full/pdf?title=how-to-choose-a-fuzzy-similarity-measure-in-decision-making
allfieldsGer	10.1108/AJEB-08-2020-0055 doi (DE-627)1758320001 (DE-599)KXP1758320001 DE-627 ger DE-627 rda eng C00 C18 C10 jelc Bouchon-Meunier, Bernadette 1948- verfasserin (DE-588)111566851 (DE-627)47100376X (DE-576)168268094 aut How to choose a fuzzy similarity measure in decision-making? Bernadette Bouchon-Meunier, Giulianella Coletti 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Purpose - The paper is dedicated to the analysis of fuzzy similarity measures in uncertainty analysis in general, and in economic decision-making in particular. The purpose of this paper is to explain how a similarity measure can be chosen to quantify a qualitative description of similarities provided by experts of a given domain, in the case where the objects to compare are described through imprecise or linguistic attribute values represented by fuzzy sets. The case of qualitative dissimilarities is also addressed and the particular case of their representation by distances is presented. Design/methodology/approach - The approach is based on measurement theory, following Tversky’s well-known paradigm. Findings - A list of axioms which may or may not be satisfied by a qualitative comparative similarity between fuzzy objects is proposed, as extensions of axioms satisfied by similarities between crisp objects. They enable to express necessary and sufficient conditions for a numerical similarity measure to represent a comparative similarity between fuzzy objects. The representation of comparative dissimilarities is also addressed by means of specific functions depending on the distance between attribute values. Originality/value - Examples of functions satisfying certain axioms to represent comparative similarities are given. They are based on the choice of operators to compute intersection, union and difference of fuzzy sets. A simple application of this methodology to economy is given, to show how a measure of similarity can be chosen to represent intuitive similarities expressed by an economist by means of a quantitative measure easily calculable. More detailed and formal results are given in Coletti and Bouchon-Meunier (2020) for similarities and Coletti et al. (2020) for dissimilarities. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/legalcode Fuzzy sets (dpeaa)DE-206 Similarity measures (dpeaa)DE-206 Dissimilarity measure (dpeaa)DE-206 Measurement theory,Qualitative choice (dpeaa)DE-206 Coletti, Giulianella verfasserin aut Enthalten in Asian journal of economics and banking Bingley : Emerald Publishing Limited, 2020 4(2020), 3, Seite 37-48 Online-Ressource (DE-627)1757052313 (DE-600)3062819-2 2615-9821 nnns volume:4 year:2020 number:3 pages:37-48 https://www.emerald.com/insight/content/doi/10.1108/AJEB-08-2020-0055/full/pdf?title=how-to-choose-a-fuzzy-similarity-measure-in-decision-making Verlag kostenfrei https://doi.org/10.1108/AJEB-08-2020-0055 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 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 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 4 2020 3 37-48 26 01 0206 3930079550 x1z 21-05-21 2403 01 DE-LFER 3936212252 00 --%%-- --%%-- n --%%-- l01 09-06-21 2403 01 DE-LFER https://doi.org/10.1108/AJEB-08-2020-0055 2403 01 DE-LFER https://www.emerald.com/insight/content/doi/10.1108/AJEB-08-2020-0055/full/pdf?title=how-to-choose-a-fuzzy-similarity-measure-in-decision-making
allfieldsSound	10.1108/AJEB-08-2020-0055 doi (DE-627)1758320001 (DE-599)KXP1758320001 DE-627 ger DE-627 rda eng C00 C18 C10 jelc Bouchon-Meunier, Bernadette 1948- verfasserin (DE-588)111566851 (DE-627)47100376X (DE-576)168268094 aut How to choose a fuzzy similarity measure in decision-making? Bernadette Bouchon-Meunier, Giulianella Coletti 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Purpose - The paper is dedicated to the analysis of fuzzy similarity measures in uncertainty analysis in general, and in economic decision-making in particular. The purpose of this paper is to explain how a similarity measure can be chosen to quantify a qualitative description of similarities provided by experts of a given domain, in the case where the objects to compare are described through imprecise or linguistic attribute values represented by fuzzy sets. The case of qualitative dissimilarities is also addressed and the particular case of their representation by distances is presented. Design/methodology/approach - The approach is based on measurement theory, following Tversky’s well-known paradigm. Findings - A list of axioms which may or may not be satisfied by a qualitative comparative similarity between fuzzy objects is proposed, as extensions of axioms satisfied by similarities between crisp objects. They enable to express necessary and sufficient conditions for a numerical similarity measure to represent a comparative similarity between fuzzy objects. The representation of comparative dissimilarities is also addressed by means of specific functions depending on the distance between attribute values. Originality/value - Examples of functions satisfying certain axioms to represent comparative similarities are given. They are based on the choice of operators to compute intersection, union and difference of fuzzy sets. A simple application of this methodology to economy is given, to show how a measure of similarity can be chosen to represent intuitive similarities expressed by an economist by means of a quantitative measure easily calculable. More detailed and formal results are given in Coletti and Bouchon-Meunier (2020) for similarities and Coletti et al. (2020) for dissimilarities. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/legalcode Fuzzy sets (dpeaa)DE-206 Similarity measures (dpeaa)DE-206 Dissimilarity measure (dpeaa)DE-206 Measurement theory,Qualitative choice (dpeaa)DE-206 Coletti, Giulianella verfasserin aut Enthalten in Asian journal of economics and banking Bingley : Emerald Publishing Limited, 2020 4(2020), 3, Seite 37-48 Online-Ressource (DE-627)1757052313 (DE-600)3062819-2 2615-9821 nnns volume:4 year:2020 number:3 pages:37-48 https://www.emerald.com/insight/content/doi/10.1108/AJEB-08-2020-0055/full/pdf?title=how-to-choose-a-fuzzy-similarity-measure-in-decision-making Verlag kostenfrei https://doi.org/10.1108/AJEB-08-2020-0055 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 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 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 4 2020 3 37-48 26 01 0206 3930079550 x1z 21-05-21 2403 01 DE-LFER 3936212252 00 --%%-- --%%-- n --%%-- l01 09-06-21 2403 01 DE-LFER https://doi.org/10.1108/AJEB-08-2020-0055 2403 01 DE-LFER https://www.emerald.com/insight/content/doi/10.1108/AJEB-08-2020-0055/full/pdf?title=how-to-choose-a-fuzzy-similarity-measure-in-decision-making
language	English
source	Enthalten in Asian journal of economics and banking 4(2020), 3, Seite 37-48 volume:4 year:2020 number:3 pages:37-48
sourceStr	Enthalten in Asian journal of economics and banking 4(2020), 3, Seite 37-48 volume:4 year:2020 number:3 pages:37-48
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topic_facet	Fuzzy sets Similarity measures Dissimilarity measure Measurement theory,Qualitative choice
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container_title	Asian journal of economics and banking
authorswithroles_txt_mv	Bouchon-Meunier, Bernadette @@aut@@ Coletti, Giulianella @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
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topic_title	C00 C18 C10 jelc How to choose a fuzzy similarity measure in decision-making? Bernadette Bouchon-Meunier, Giulianella Coletti Fuzzy sets (dpeaa)DE-206 Similarity measures (dpeaa)DE-206 Dissimilarity measure (dpeaa)DE-206 Measurement theory,Qualitative choice (dpeaa)DE-206
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title_full	How to choose a fuzzy similarity measure in decision-making? Bernadette Bouchon-Meunier, Giulianella Coletti
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title_sort	how to choose a fuzzy similarity measure in decision-making?
title_auth	How to choose a fuzzy similarity measure in decision-making?
abstract	Purpose - The paper is dedicated to the analysis of fuzzy similarity measures in uncertainty analysis in general, and in economic decision-making in particular. The purpose of this paper is to explain how a similarity measure can be chosen to quantify a qualitative description of similarities provided by experts of a given domain, in the case where the objects to compare are described through imprecise or linguistic attribute values represented by fuzzy sets. The case of qualitative dissimilarities is also addressed and the particular case of their representation by distances is presented. Design/methodology/approach - The approach is based on measurement theory, following Tversky’s well-known paradigm. Findings - A list of axioms which may or may not be satisfied by a qualitative comparative similarity between fuzzy objects is proposed, as extensions of axioms satisfied by similarities between crisp objects. They enable to express necessary and sufficient conditions for a numerical similarity measure to represent a comparative similarity between fuzzy objects. The representation of comparative dissimilarities is also addressed by means of specific functions depending on the distance between attribute values. Originality/value - Examples of functions satisfying certain axioms to represent comparative similarities are given. They are based on the choice of operators to compute intersection, union and difference of fuzzy sets. A simple application of this methodology to economy is given, to show how a measure of similarity can be chosen to represent intuitive similarities expressed by an economist by means of a quantitative measure easily calculable. More detailed and formal results are given in Coletti and Bouchon-Meunier (2020) for similarities and Coletti et al. (2020) for dissimilarities.
abstractGer	Purpose - The paper is dedicated to the analysis of fuzzy similarity measures in uncertainty analysis in general, and in economic decision-making in particular. The purpose of this paper is to explain how a similarity measure can be chosen to quantify a qualitative description of similarities provided by experts of a given domain, in the case where the objects to compare are described through imprecise or linguistic attribute values represented by fuzzy sets. The case of qualitative dissimilarities is also addressed and the particular case of their representation by distances is presented. Design/methodology/approach - The approach is based on measurement theory, following Tversky’s well-known paradigm. Findings - A list of axioms which may or may not be satisfied by a qualitative comparative similarity between fuzzy objects is proposed, as extensions of axioms satisfied by similarities between crisp objects. They enable to express necessary and sufficient conditions for a numerical similarity measure to represent a comparative similarity between fuzzy objects. The representation of comparative dissimilarities is also addressed by means of specific functions depending on the distance between attribute values. Originality/value - Examples of functions satisfying certain axioms to represent comparative similarities are given. They are based on the choice of operators to compute intersection, union and difference of fuzzy sets. A simple application of this methodology to economy is given, to show how a measure of similarity can be chosen to represent intuitive similarities expressed by an economist by means of a quantitative measure easily calculable. More detailed and formal results are given in Coletti and Bouchon-Meunier (2020) for similarities and Coletti et al. (2020) for dissimilarities.
abstract_unstemmed	Purpose - The paper is dedicated to the analysis of fuzzy similarity measures in uncertainty analysis in general, and in economic decision-making in particular. The purpose of this paper is to explain how a similarity measure can be chosen to quantify a qualitative description of similarities provided by experts of a given domain, in the case where the objects to compare are described through imprecise or linguistic attribute values represented by fuzzy sets. The case of qualitative dissimilarities is also addressed and the particular case of their representation by distances is presented. Design/methodology/approach - The approach is based on measurement theory, following Tversky’s well-known paradigm. Findings - A list of axioms which may or may not be satisfied by a qualitative comparative similarity between fuzzy objects is proposed, as extensions of axioms satisfied by similarities between crisp objects. They enable to express necessary and sufficient conditions for a numerical similarity measure to represent a comparative similarity between fuzzy objects. The representation of comparative dissimilarities is also addressed by means of specific functions depending on the distance between attribute values. Originality/value - Examples of functions satisfying certain axioms to represent comparative similarities are given. They are based on the choice of operators to compute intersection, union and difference of fuzzy sets. A simple application of this methodology to economy is given, to show how a measure of similarity can be chosen to represent intuitive similarities expressed by an economist by means of a quantitative measure easily calculable. More detailed and formal results are given in Coletti and Bouchon-Meunier (2020) for similarities and Coletti et al. (2020) for dissimilarities.
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