Fuzzy Decision-Making Methods Based on Prospect Theory and Its Application in Venture Capital

ISBN-13: 9789811602429 / Angielski / Twarda / 2021 / 152 str.

Xiaoli Tian; Zeshui Xu

Spis treści

Preface 1

Chapter 1. Introduction       1

1.1 Background 1

1.1.1 Development of bounded rationality      2

1.1.2 Development of fuzzy information          3

1.1.3 Importance of research about fuzzy decision making with prospect theory           3

1.2 Corresponding preliminaries                4

1.2.1 Prospect theory                5

1.2.2 TODIM  5

1.2.3 Intuitionistic fuzzy information   7

1.2.4 Probabilistic hesitant fuzzy information  9

1.2.5 Hesitant fuzzy linguistic information        11

1.2.6 Probabilistic linguistic information             14

1.3 Aim and focus of this book   17

Chapter 2. Intuitionistic Fuzzy MADM based on PT  19

2.1 Decision-making procedure 20

2.2 Illustrative example 24

2.2.1 Decision-making attributes used by VCs 26

2.2.2 Selecting process and results derived by IFPT      28

2.2.3. Selecting process and results derived by TOPSIS               30

2.3. Remarks      33

Chapter 3. QUALIFLEX based on PT with Probabilistic Linguistic Information 35

3.1 Procedure of P-QUALIFLEX with probabilistic linguistic information    36

3.2 Procedure of the extended QUALIFLEX with probabilistic linguistic information           39

3.3 Illustrative example 41

3.3.1 Results of P-QUALIFLEX with probabilistic linguistic information  42

3.3.2 Results of the extended QUALIFLEX with probabilistic linguistic information         46

3.4 Comparative analysis              48

3.4.1 Comparison of P-QUALIFLEX with extended QUALIFLEX 48

3.4.2 Comparison of P-QUALIFLEX with TODIM              50

3.5 Remarks       55

Chapter 4. Group PROMETHEE based on PT with Hesitant Fuzzy Linguistic Information          57

4.1 GP-PROMETHEE with hesitant fuzzy linguistic information     60

4.2 G-PROMETHEE with hesitant fuzzy linguistic information        65

4.3 Illustrative example 67

4.3.1 Decision-making background      67

4.3.2 Results of the GP-PROMETHEE with hesitant fuzzy linguistic information                69

4.3.3 Results of the G-PROMETHEE with hesitant fuzzy linguistic information  75

4.3.4 Results of TODIM with hesitant fuzzy linguistic information          78

4.3.5 Comparative analysis      80

4.3.5.1 Comparative analysis based on the results of illustrative example     81

4.3.5.2 Comparative analysis based on the sensitivity of parameters              82

4.4 Simulation analysis   88

4.5 Remarks       91

Chapter 5. Prospect Consensus with Probabilistic Hesitant Fuzzy Preference Information    93

5.1 Probabilistic hesitant fuzzy preference information  93

5.2 Consensus model based on PT with P-HFPs  95

5.2.1 Prospect consensus measure with P-HFPs            96

5.2.2 Procedure of reaching prospect consensus and decision-making               100

5.3 Illustrative example 102

5.3.1 Sequential decision-making attributes    103

5.3.2 Results of prospect consensus with P-HFPs          106

5.3.3 Results of the expected consensus process with P-HFPs                114

5.3.4 Results of prospect consensus with HFPs              117

5.3.5 Results of the expected consensus with HFPs     120

5.3.6 Comparative analysis      121

5.4 Simulated analysis    124

5.5 Remarks       131

Chapter 6. An Improved TODIM based on PT             132

6.1 Procedure of the improved TODIM  133

6.2 Illustrative example 135

6.2.1 Decision-making background      135

6.2.2 Results of the improved TODIM 136

6.2.3 Results of the classical TODIM    139

6.2.4 Comparative analysis between the improved and the classical TODIM     140

6.3 Remarks       141

Chapter 7. An improved TODIM with probabilistic hesitant fuzzy information             143

7.1 Procedure of the improved TODIM with probabilistic hesitant fuzzy information         143

7.2 Procedure of the improved TODIM with hesitant fuzzy information  145

7.3 Illustrative analysis   148

7.3.1 Screening process of the improved TODIM with probabilistic hesitant fuzzy information 148

7.3.2 Screening process of the extended TODIM with probabilistic hesitant fuzzy information 150

7.3.3 Screening process of the improved TODIM with hesitant fuzzy information          153

7.3.4 Screening process of the extended TODIM with hesitant fuzzy information          154

7.3.5 Analysis                157

7.4 Comparative analysis              159

7.4.1 Comparative analysis with the TOPSIS method   159

7.4.2 Sensitivity analysis based on the parameter values           162

7.4.2.1 Sensitivity analysis of the improved TODIM and the extended TODIM with the same fuzzy information        162

7.4.2.2 Sensitivity analysis of the improved TODIM and the extended TODIM based on different types of fuzzy information       165

7.5 Simulation analysis   171

7.6 Remarks       173

Chapter 8. Conclusions        175

8.1 Summary      175

8.2 Future studies            178

References:              181

 

O autorze

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 Chang Jiang 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 World’s Top 100,000 Scientists in 2019. He has contributed more than 600 SCI/SSCI articles to professional journals, and is among the world’s 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.

Blurb

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.