The Probabilistic Hesitant Fuzzy TOPSIS Method Based on the Regret Theory and Its Application in Investment Strategy Article Swipe
Chenyang Song
,
Zeshui Xu
,
Jian Hou
,
Jianchao Ji
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2851198/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2851198/v1
The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is a popular multi-attribute decision making method. However, the increasing uncertain information with probability and the psychological factor of regret aversion of experts in some situations bring new challenges to the application of classic TOPSIS. This paper expands the application of regret theory to the probabilistic hesitant fuzzy environment and proposes the corresponding concepts of utility function, reject-rejoice function and perceived utility value of the probabilistic hesitant fuzzy element (P-HFE). The maximum deviation model under the probabilistic hesitant fuzzy environment is presented to determine the weights of attributes. Based on which, we propose a new probabilistic hesitant fuzzy TOPSIS (PHFTOPSIS) method based on the regret theory. The detailed implementation process of the PHFTOPSIS method based on the regret theory is also provided. Moreover, we apply the proposed PHFTOPSIS method based on the regret theory to the investment strategy. A comparative analysis with traditional TOPSIS and probabilistic hesitant fuzzy weighted averaging (PHFWA) operator is further conducted to illustrate its advantages.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-2851198/v1
- https://www.researchsquare.com/article/rs-2851198/latest.pdf
- OA Status
- green
- Cited By
- 1
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4376280492
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- OpenAlex ID
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https://openalex.org/W4376280492Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-2851198/v1Digital Object Identifier
- Title
-
The Probabilistic Hesitant Fuzzy TOPSIS Method Based on the Regret Theory and Its Application in Investment StrategyWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-12Full publication date if available
- Authors
-
Chenyang Song, Zeshui Xu, Jian Hou, Jianchao JiList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-2851198/v1Publisher landing page
- PDF URL
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https://www.researchsquare.com/article/rs-2851198/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-2851198/latest.pdfDirect OA link when available
- Concepts
-
Regret, Probabilistic logic, TOPSIS, Fuzzy logic, Investment (military), Computer science, Mathematical optimization, Operations research, Mathematical economics, Economics, Business, Mathematics, Artificial intelligence, Machine learning, Political science, Politics, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- References (count)
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28Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.to | 9, 41, 55, 94, 146, 167 |
| abstract_inverted_index.we | 103, 135 |
| abstract_inverted_index.The | 1, 82, 118 |
| abstract_inverted_index.and | 26, 61, 71, 156 |
| abstract_inverted_index.for | 4 |
| abstract_inverted_index.its | 169 |
| abstract_inverted_index.new | 39, 106 |
| abstract_inverted_index.the | 20, 27, 42, 50, 56, 63, 76, 87, 96, 115, 123, 128, 137, 143, 147 |
| abstract_inverted_index.This | 47 |
| abstract_inverted_index.also | 132 |
| abstract_inverted_index.some | 36 |
| abstract_inverted_index.with | 24, 153 |
| abstract_inverted_index.Based | 100 |
| abstract_inverted_index.Ideal | 10 |
| abstract_inverted_index.Order | 5 |
| abstract_inverted_index.apply | 136 |
| abstract_inverted_index.based | 113, 126, 141 |
| abstract_inverted_index.bring | 38 |
| abstract_inverted_index.fuzzy | 59, 79, 90, 109, 159 |
| abstract_inverted_index.model | 85 |
| abstract_inverted_index.paper | 48 |
| abstract_inverted_index.under | 86 |
| abstract_inverted_index.value | 74 |
| abstract_inverted_index.TOPSIS | 2, 110, 155 |
| abstract_inverted_index.factor | 29 |
| abstract_inverted_index.making | 17 |
| abstract_inverted_index.method | 112, 125, 140 |
| abstract_inverted_index.regret | 31, 53, 116, 129, 144 |
| abstract_inverted_index.theory | 54, 130, 145 |
| abstract_inverted_index.which, | 102 |
| abstract_inverted_index.(PHFWA) | 162 |
| abstract_inverted_index.TOPSIS. | 46 |
| abstract_inverted_index.classic | 45 |
| abstract_inverted_index.element | 80 |
| abstract_inverted_index.expands | 49 |
| abstract_inverted_index.experts | 34 |
| abstract_inverted_index.further | 165 |
| abstract_inverted_index.maximum | 83 |
| abstract_inverted_index.method. | 18 |
| abstract_inverted_index.popular | 14 |
| abstract_inverted_index.process | 121 |
| abstract_inverted_index.propose | 104 |
| abstract_inverted_index.theory. | 117 |
| abstract_inverted_index.utility | 67, 73 |
| abstract_inverted_index.weights | 97 |
| abstract_inverted_index.(P-HFE). | 81 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 19 |
| abstract_inverted_index.analysis | 152 |
| abstract_inverted_index.aversion | 32 |
| abstract_inverted_index.concepts | 65 |
| abstract_inverted_index.decision | 16 |
| abstract_inverted_index.detailed | 119 |
| abstract_inverted_index.function | 70 |
| abstract_inverted_index.hesitant | 58, 78, 89, 108, 158 |
| abstract_inverted_index.operator | 163 |
| abstract_inverted_index.proposed | 138 |
| abstract_inverted_index.proposes | 62 |
| abstract_inverted_index.weighted | 160 |
| abstract_inverted_index.Moreover, | 134 |
| abstract_inverted_index.PHFTOPSIS | 124, 139 |
| abstract_inverted_index.Solution) | 11 |
| abstract_inverted_index.averaging | 161 |
| abstract_inverted_index.conducted | 166 |
| abstract_inverted_index.determine | 95 |
| abstract_inverted_index.deviation | 84 |
| abstract_inverted_index.function, | 68 |
| abstract_inverted_index.perceived | 72 |
| abstract_inverted_index.presented | 93 |
| abstract_inverted_index.provided. | 133 |
| abstract_inverted_index.strategy. | 149 |
| abstract_inverted_index.uncertain | 22 |
| abstract_inverted_index.(Technique | 3 |
| abstract_inverted_index.Preference | 6 |
| abstract_inverted_index.Similarity | 8 |
| abstract_inverted_index.challenges | 40 |
| abstract_inverted_index.illustrate | 168 |
| abstract_inverted_index.increasing | 21 |
| abstract_inverted_index.investment | 148 |
| abstract_inverted_index.situations | 37 |
| abstract_inverted_index.(PHFTOPSIS) | 111 |
| abstract_inverted_index.advantages. | 170 |
| abstract_inverted_index.application | 43, 51 |
| abstract_inverted_index.attributes. | 99 |
| abstract_inverted_index.comparative | 151 |
| abstract_inverted_index.environment | 60, 91 |
| abstract_inverted_index.information | 23 |
| abstract_inverted_index.probability | 25 |
| abstract_inverted_index.traditional | 154 |
| abstract_inverted_index.corresponding | 64 |
| abstract_inverted_index.probabilistic | 57, 77, 88, 107, 157 |
| abstract_inverted_index.psychological | 28 |
| abstract_inverted_index.implementation | 120 |
| abstract_inverted_index.reject-rejoice | 69 |
| abstract_inverted_index.multi-attribute | 15 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.56915143 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |