This book gives a thorough and systematic introduction to the latest research results about fuzzy decision-making method based on prospect theory. It includes eight chapters: Introduction, Intuitionistic fuzzy MADM based on prospect theory, QUALIFLEX based on prospect theory with probabilistic linguistic information, Group PROMETHEE based on prospect theory with hesitant fuzzy linguistic information, Prospect consensus with probabilistic hesitant fuzzy preference information, Improved TODIM based on prospect theory and the improved TODIM with probabilistic hesitant fuzzy information, etc. This book is suitable for the researchers in the fields of fuzzy mathematics, operations research, behavioral science, management science and engineering, etc. It is also useful as a textbook for postgraduate and senior-year undergraduate students of the relevant professional institutions of higher learning.
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1 | (16) |
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1 | (3) |
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1.1.1 Development of Bounded Rationality |
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2 | (1) |
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1.1.2 Development of Fuzzy Information |
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2 | (1) |
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1.1.3 Importance of Research About Fuzzy Decision Making with FT |
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3 | (1) |
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1.2 Corresponding Preliminaries |
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4 | (9) |
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4 | (1) |
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5 | (1) |
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1.2.3 Intuitionistic Fuzzy Information |
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6 | (1) |
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1.2.4 Probabilistic Hesitant Fuzzy Information |
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7 | (2) |
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1.2.5 Hesitant Fuzzy Linguistic Information |
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9 | (2) |
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1.2.6 Probabilistic Linguistic Information |
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11 | (2) |
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1.3 Aim and Focus of This Book |
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13 | (4) |
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14 | (3) |
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2 Intuitionistic Fuzzy MADM Based on PT |
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17 | (14) |
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2.1 Decision-Making Procedure |
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18 | (3) |
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21 | (6) |
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2.2.1 Decision-Making Attributes Used by VCs |
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22 | (1) |
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2.2.2 Selecting Process and Results Derived by IFPT |
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23 | (2) |
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2.2.3 Selecting Process and Results Derived by TOPSIS |
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25 | (2) |
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27 | (4) |
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28 | (3) |
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3 QUALIFLEX Based on PT with Probabilistic Linguistic Information |
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31 | (18) |
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3.1 Procedure of P-QUALIFLEX with Probabilistic Linguistic Information |
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32 | (3) |
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3.2 Procedure of the Extended QUALIFLEX with Probabilistic Linguistic Information |
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35 | (1) |
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36 | (5) |
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3.3.1 Results of P-QUALIFLEX with Probabilistic Linguistic Information |
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37 | (3) |
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3.3.2 Results of the Extended QUALIFLEX with Probabilistic Linguistic Information |
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40 | (1) |
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41 | (5) |
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3.4.1 Comparison of P-QUALIFLEX with Extended QUALIFLEX |
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41 | (2) |
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3.4.2 Comparison of P-QUALIFLEX with TODIM |
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43 | (3) |
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46 | (3) |
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47 | (2) |
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4 Group PROMETHEE Based on PT with Hesitant Fuzzy Linguistic Information |
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49 | (30) |
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4.1 GP-PROMETHEE with Hesitant Fuzzy Linguistic Information |
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51 | (3) |
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4.2 G-PROMETHEE with Hesitant Fuzzy Linguistic Information |
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54 | (2) |
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56 | (17) |
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4.3.1 Decision-Making Background |
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56 | (2) |
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4.3.2 Results of the GP-PROMETHEE with Hesitant Fuzzy Linguistic Information |
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58 | (4) |
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4.3.3 Results of the G-PROMETHEE with Hesitant Fuzzy Linguistic Information |
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62 | (1) |
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4.3.4 Results of TODIM with Hesitant Fuzzy Linguistic Information |
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63 | (4) |
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4.3.5 Comparative Analysis |
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67 | (6) |
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73 | (1) |
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74 | (5) |
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77 | (2) |
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5 Prospect Consensus with Probabilistic Hesitant Fuzzy Preference Information |
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79 | (32) |
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5.1 Probabilistic Hesitant Fuzzy Preference Information |
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79 | (1) |
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5.2 Consensus Model Based on PT with P-HFPs |
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80 | (6) |
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5.2.1 Prospect Consensus Measure with P-HFPs |
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81 | (3) |
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5.2.2 Procedure of Reaching Prospect Consensus and Decision-Making |
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84 | (2) |
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86 | (20) |
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5.3.1 Sequential Decision-Making Attributes |
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86 | (2) |
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5.3.2 Results of Prospect Consensus with P-HFPs |
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88 | (8) |
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5.3.3 Results of the Expected Consensus Process with P-HFPs |
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96 | (4) |
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5.3.4 Results of Prospect Consensus with HFPs |
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100 | (3) |
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5.3.5 Results of the Expected Consensus with HFPs |
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103 | (1) |
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5.3.6 Comparative Analysis |
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103 | (3) |
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106 | (4) |
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110 | (1) |
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110 | (1) |
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6 An Improved TODIM Based on PT |
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111 | (10) |
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6.1 Procedure of the Improved TODIM |
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112 | (1) |
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113 | (6) |
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6.2.1 Decision-Making Background |
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113 | (1) |
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6.2.2 Results of the Improved TODIM |
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114 | (3) |
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6.2.3 Results of the Classical TODIM |
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117 | (1) |
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6.2.4 Comparative Analysis Between the Improved and the Classical TODIM |
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118 | (1) |
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119 | (2) |
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119 | (2) |
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7 An Improved TODIM with Probabilistic Hesitant Fuzzy Information |
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121 | (28) |
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7.1 Procedure of the Improved TODIM with Probabilistic Hesitant Fuzzy Information |
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121 | (2) |
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7.2 Procedure of the Improved TODIM with Hesitant Fuzzy Information |
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123 | (2) |
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7.3 Illustrative Analysis |
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125 | (9) |
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7.3.1 Screening Process of the Improved TODIM with Probabilistic Hesitant Fuzzy Information |
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125 | (1) |
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7.3.2 Screening Process of the Extended TODIM with Probabilistic Hesitant Fuzzy Information |
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126 | (3) |
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7.3.3 Screening Process of the Improved TODIM with Hesitant Fuzzy Information |
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129 | (1) |
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7.3.4 Screening Process of the Extended TODIM with Hesitant Fuzzy Information |
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130 | (3) |
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133 | (1) |
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134 | (10) |
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7.4.1 Comparative Analysis with the TOPSIS Method |
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134 | (3) |
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7.4.2 Sensitivity Analysis Based on the Parameter Values |
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137 | (7) |
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144 | (3) |
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147 | (2) |
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148 | (1) |
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149 | (3) |
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149 | (2) |
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151 | (1) |
| Reference |
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152 | |
Xiaoli Tian is Associate Professor of the School of Business Administration in Southwestern University of Finance and Economics, Chengdu, China. She was Academic Visitor with the Department of Computer Science and Artificial Intelligence, University of Granada, Spain, in 2017. She has published more than 15 peer-reviewed papers, many in high-quality international journals including Knowledge-Based Systems, Applied Soft Computing, Technological and Economic Development of Economy, Technological Forecasting and Social Change, etc. One of her papers has been selected as ESI Highly Cited Papers. Her current research interest includes large-scale consensus, group decision making, decision making with bounded rationality, and multiple attributes decision making under uncertainty. Dr. Tian serves as a reviewer for more than 10 international journals.
Zeshui Xu is Distinguished Young Scholar of the National Natural Science Foundation of China and ChangJiang Scholars of the Ministry of Education of China. He is currently Professor with the Business School, Sichuan University, Chengdu, China. He has been elected as Academician of IASCYS (International academy for systems and cybernetic sciences), Fellow of IEEE (Institute of Electrical and Electronics Engineers), Fellow of IFSA (International Fuzzy Systems Association), Fellow of RSA (Royal Society of Arts), Fellow of IET (Institution of Engineering and Technology), Fellow of BCS (British Computer Society), Fellow of IAAM (International Association of Advanced Materials), Fellow of VEBLEO, and ranked 431th among Worlds Top 100,000 Scientists in 2019. He has contributed more than 600 SCI/SSCI articles to professional journals, and is among the worlds top 1% most highly cited researchers with about 62,000 citations, his h-index is 123. He is currently the Associate Editors of IEEE Transactions on Cybernetics, IEEE Transactions on Fuzzy Systems, IEEE Access, Information Sciences, Fuzzy Optimization and Decision Making, Journal of the Operational Research Socitey, International Journal of Systems Science, Artificial Intelligence Review, etc. His current research interests include decision making, information fusion, data analysis, fuzzy systems and applications.
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