Inverse Preference Optimization in the Graph Model for Conflict Resolution based on the Genetic Algorithm
Abstract The Inverse GMCR (Graph Model for Conflict Resolution) produces rankings of possible states (preference relation profiles) that will make the desired resolution of a conflict stable. However, there are usually numerous preference relation profiles making it difficult for a third party to ch...
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| Autor*in: |
Tao, Liangyan [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature B.V. 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Group decision and negotiation - Springer Netherlands, 1992, 30(2021), 5 vom: 09. Juni, Seite 1085-1112 |
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| Übergeordnetes Werk: |
volume:30 ; year:2021 ; number:5 ; day:09 ; month:06 ; pages:1085-1112 |
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| DOI / URN: |
10.1007/s10726-021-09748-9 |
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OLC2127598563 |
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| 520 | |a Abstract The Inverse GMCR (Graph Model for Conflict Resolution) produces rankings of possible states (preference relation profiles) that will make the desired resolution of a conflict stable. However, there are usually numerous preference relation profiles making it difficult for a third party to choose an appropriate preference relation to design its mediation strategy. Moreover, the cost or effort of changing preference relations over states has rarely been studied in Inverse GMCR. The current study presents two inverse preference optimization models considering the cost and effort in changing preferences to address these issues. The first model aims to ascertain an optimal preference at minimum adjustment cost such that the desired equilibrium is reached. The other model is to find an optimal required preference under minimum adjustment amount, which is defined as the difference between the required preference matrix and the original preference matrix. Then, a Genetic Algorithm (GA)-based algorithm is proposed. Finally, the two proposed preference optimization methods are applied to two cases, demonstrating the effectiveness of the proposed methodology. | ||
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10.1007/s10726-021-09748-9 doi (DE-627)OLC2127598563 (DE-He213)s10726-021-09748-9-p DE-627 ger DE-627 rakwb eng 150 300 650 VZ 5,2 3,4 3,2 ssgn Tao, Liangyan verfasserin aut Inverse Preference Optimization in the Graph Model for Conflict Resolution based on the Genetic Algorithm 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2021 Abstract The Inverse GMCR (Graph Model for Conflict Resolution) produces rankings of possible states (preference relation profiles) that will make the desired resolution of a conflict stable. However, there are usually numerous preference relation profiles making it difficult for a third party to choose an appropriate preference relation to design its mediation strategy. Moreover, the cost or effort of changing preference relations over states has rarely been studied in Inverse GMCR. The current study presents two inverse preference optimization models considering the cost and effort in changing preferences to address these issues. The first model aims to ascertain an optimal preference at minimum adjustment cost such that the desired equilibrium is reached. The other model is to find an optimal required preference under minimum adjustment amount, which is defined as the difference between the required preference matrix and the original preference matrix. Then, a Genetic Algorithm (GA)-based algorithm is proposed. Finally, the two proposed preference optimization methods are applied to two cases, demonstrating the effectiveness of the proposed methodology. Group decision and negotiation Inverse GMCR Preference optimization Conflict Resolution Genetic Algorithm Graph Model for Conflict Resolution Su, Xuebi aut Javed, Saad Ahmed (orcid)0000-0002-7916-7537 aut Enthalten in Group decision and negotiation Springer Netherlands, 1992 30(2021), 5 vom: 09. Juni, Seite 1085-1112 (DE-627)17112684X (DE-600)1155213-X (DE-576)040094448 0926-2644 nnns volume:30 year:2021 number:5 day:09 month:06 pages:1085-1112 https://doi.org/10.1007/s10726-021-09748-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW AR 30 2021 5 09 06 1085-1112 |
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10.1007/s10726-021-09748-9 doi (DE-627)OLC2127598563 (DE-He213)s10726-021-09748-9-p DE-627 ger DE-627 rakwb eng 150 300 650 VZ 5,2 3,4 3,2 ssgn Tao, Liangyan verfasserin aut Inverse Preference Optimization in the Graph Model for Conflict Resolution based on the Genetic Algorithm 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2021 Abstract The Inverse GMCR (Graph Model for Conflict Resolution) produces rankings of possible states (preference relation profiles) that will make the desired resolution of a conflict stable. However, there are usually numerous preference relation profiles making it difficult for a third party to choose an appropriate preference relation to design its mediation strategy. Moreover, the cost or effort of changing preference relations over states has rarely been studied in Inverse GMCR. The current study presents two inverse preference optimization models considering the cost and effort in changing preferences to address these issues. The first model aims to ascertain an optimal preference at minimum adjustment cost such that the desired equilibrium is reached. The other model is to find an optimal required preference under minimum adjustment amount, which is defined as the difference between the required preference matrix and the original preference matrix. Then, a Genetic Algorithm (GA)-based algorithm is proposed. Finally, the two proposed preference optimization methods are applied to two cases, demonstrating the effectiveness of the proposed methodology. Group decision and negotiation Inverse GMCR Preference optimization Conflict Resolution Genetic Algorithm Graph Model for Conflict Resolution Su, Xuebi aut Javed, Saad Ahmed (orcid)0000-0002-7916-7537 aut Enthalten in Group decision and negotiation Springer Netherlands, 1992 30(2021), 5 vom: 09. Juni, Seite 1085-1112 (DE-627)17112684X (DE-600)1155213-X (DE-576)040094448 0926-2644 nnns volume:30 year:2021 number:5 day:09 month:06 pages:1085-1112 https://doi.org/10.1007/s10726-021-09748-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW AR 30 2021 5 09 06 1085-1112 |
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Abstract The Inverse GMCR (Graph Model for Conflict Resolution) produces rankings of possible states (preference relation profiles) that will make the desired resolution of a conflict stable. However, there are usually numerous preference relation profiles making it difficult for a third party to choose an appropriate preference relation to design its mediation strategy. Moreover, the cost or effort of changing preference relations over states has rarely been studied in Inverse GMCR. The current study presents two inverse preference optimization models considering the cost and effort in changing preferences to address these issues. The first model aims to ascertain an optimal preference at minimum adjustment cost such that the desired equilibrium is reached. The other model is to find an optimal required preference under minimum adjustment amount, which is defined as the difference between the required preference matrix and the original preference matrix. Then, a Genetic Algorithm (GA)-based algorithm is proposed. Finally, the two proposed preference optimization methods are applied to two cases, demonstrating the effectiveness of the proposed methodology. © The Author(s), under exclusive licence to Springer Nature B.V. 2021 |
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Abstract The Inverse GMCR (Graph Model for Conflict Resolution) produces rankings of possible states (preference relation profiles) that will make the desired resolution of a conflict stable. However, there are usually numerous preference relation profiles making it difficult for a third party to choose an appropriate preference relation to design its mediation strategy. Moreover, the cost or effort of changing preference relations over states has rarely been studied in Inverse GMCR. The current study presents two inverse preference optimization models considering the cost and effort in changing preferences to address these issues. The first model aims to ascertain an optimal preference at minimum adjustment cost such that the desired equilibrium is reached. The other model is to find an optimal required preference under minimum adjustment amount, which is defined as the difference between the required preference matrix and the original preference matrix. Then, a Genetic Algorithm (GA)-based algorithm is proposed. Finally, the two proposed preference optimization methods are applied to two cases, demonstrating the effectiveness of the proposed methodology. © The Author(s), under exclusive licence to Springer Nature B.V. 2021 |
| abstract_unstemmed |
Abstract The Inverse GMCR (Graph Model for Conflict Resolution) produces rankings of possible states (preference relation profiles) that will make the desired resolution of a conflict stable. However, there are usually numerous preference relation profiles making it difficult for a third party to choose an appropriate preference relation to design its mediation strategy. Moreover, the cost or effort of changing preference relations over states has rarely been studied in Inverse GMCR. The current study presents two inverse preference optimization models considering the cost and effort in changing preferences to address these issues. The first model aims to ascertain an optimal preference at minimum adjustment cost such that the desired equilibrium is reached. The other model is to find an optimal required preference under minimum adjustment amount, which is defined as the difference between the required preference matrix and the original preference matrix. Then, a Genetic Algorithm (GA)-based algorithm is proposed. Finally, the two proposed preference optimization methods are applied to two cases, demonstrating the effectiveness of the proposed methodology. © The Author(s), under exclusive licence to Springer Nature B.V. 2021 |
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Inverse Preference Optimization in the Graph Model for Conflict Resolution based on the Genetic Algorithm |
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Su, Xuebi Javed, Saad Ahmed |
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