When Fairness Meets Consistency in AHP Pairwise Comparisons
We propose introducing fairness constraints to one of the most famous multi-criteria decision-making methods, the analytic hierarchy process (AHP). We offer a solution that guarantees consistency while respecting legally binding fairness constraints in AHP pairwise comparison matrices. Through a syn...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/3/604 |
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| author | Zorica Dodevska Sandro Radovanović Andrija Petrović Boris Delibašić |
| author_facet | Zorica Dodevska Sandro Radovanović Andrija Petrović Boris Delibašić |
| author_sort | Zorica Dodevska |
| collection | DOAJ |
| description | We propose introducing fairness constraints to one of the most famous multi-criteria decision-making methods, the analytic hierarchy process (AHP). We offer a solution that guarantees consistency while respecting legally binding fairness constraints in AHP pairwise comparison matrices. Through a synthetic experiment, we generate the comparison matrices of different sizes and ranges/levels of the initial parameters (i.e., consistency ratio and disparate impact). We optimize disparate impact for various combinations of these initial parameters and observed matrix sizes while respecting an acceptable level of consistency and minimizing deviations of pairwise comparison matrices (or their upper triangles) before and after the optimization. We use a metaheuristic genetic algorithm to set the dually motivating problem and operate a discrete optimization procedure (in connection with Saaty’s 9-point scale). The results confirm the initial hypothesis (with 99.5% validity concerning 2800 optimization runs) that achieving fair ranking while respecting consistency in AHP pairwise comparison matrices (when comparing alternatives regarding given criterium) is possible, thus meeting two challenging goals simultaneously. This research contributes to the initiatives directed toward unbiased decision-making, either automated or algorithm-assisted (which is the case covered by this research). |
| first_indexed | 2024-03-11T09:34:08Z |
| format | Article |
| id | doaj.art-2fd73f56e5104f29b23d31314e3bb5fc |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T09:34:08Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-2fd73f56e5104f29b23d31314e3bb5fc2023-11-16T17:21:58ZengMDPI AGMathematics2227-73902023-01-0111360410.3390/math11030604When Fairness Meets Consistency in AHP Pairwise ComparisonsZorica Dodevska0Sandro Radovanović1Andrija Petrović2Boris Delibašić3The Institute for Artificial Intelligence Research and Development of Serbia, 1 Fruškogorska, 21000 Novi Sad, SerbiaFaculty of Organizational Sciences, The University of Belgrade, 154 Jove Ilića, 11000 Belgrade, SerbiaFaculty of Organizational Sciences, The University of Belgrade, 154 Jove Ilića, 11000 Belgrade, SerbiaFaculty of Organizational Sciences, The University of Belgrade, 154 Jove Ilića, 11000 Belgrade, SerbiaWe propose introducing fairness constraints to one of the most famous multi-criteria decision-making methods, the analytic hierarchy process (AHP). We offer a solution that guarantees consistency while respecting legally binding fairness constraints in AHP pairwise comparison matrices. Through a synthetic experiment, we generate the comparison matrices of different sizes and ranges/levels of the initial parameters (i.e., consistency ratio and disparate impact). We optimize disparate impact for various combinations of these initial parameters and observed matrix sizes while respecting an acceptable level of consistency and minimizing deviations of pairwise comparison matrices (or their upper triangles) before and after the optimization. We use a metaheuristic genetic algorithm to set the dually motivating problem and operate a discrete optimization procedure (in connection with Saaty’s 9-point scale). The results confirm the initial hypothesis (with 99.5% validity concerning 2800 optimization runs) that achieving fair ranking while respecting consistency in AHP pairwise comparison matrices (when comparing alternatives regarding given criterium) is possible, thus meeting two challenging goals simultaneously. This research contributes to the initiatives directed toward unbiased decision-making, either automated or algorithm-assisted (which is the case covered by this research).https://www.mdpi.com/2227-7390/11/3/604analytic hierarchy process (AHP)fairnessconsistencymulti-criteria decision-making (MCDM)decision-making algorithmsdiscrete optimization |
| spellingShingle | Zorica Dodevska Sandro Radovanović Andrija Petrović Boris Delibašić When Fairness Meets Consistency in AHP Pairwise Comparisons Mathematics analytic hierarchy process (AHP) fairness consistency multi-criteria decision-making (MCDM) decision-making algorithms discrete optimization |
| title | When Fairness Meets Consistency in AHP Pairwise Comparisons |
| title_full | When Fairness Meets Consistency in AHP Pairwise Comparisons |
| title_fullStr | When Fairness Meets Consistency in AHP Pairwise Comparisons |
| title_full_unstemmed | When Fairness Meets Consistency in AHP Pairwise Comparisons |
| title_short | When Fairness Meets Consistency in AHP Pairwise Comparisons |
| title_sort | when fairness meets consistency in ahp pairwise comparisons |
| topic | analytic hierarchy process (AHP) fairness consistency multi-criteria decision-making (MCDM) decision-making algorithms discrete optimization |
| url | https://www.mdpi.com/2227-7390/11/3/604 |
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