Robust Group Consensus Decision Making
This book provides a systematic and model-driven treatment of robust consensus reaching in group decision making under uncertain costs, social network interactions, and heterogeneous decision behaviors. It develops a progressive framework from fundamental concepts to robust optimization models, social network-based consensus mechanisms, and integrated data-driven consensus reaching approaches.
The book first introduces the foundations of group consensus decision making and robust optimization, establishing the theoretical basis for modeling uncertainty in consensus processes. It then presents a series of robust consensus models with uncertain adjustment costs, including maximum expert consensus models with adjustment paths, quadratic cost structures, and large-scale group decision-making settings. Further, the book extends consensus modeling to social network environments by incorporating trust relationships, risk preferences, conflict constraints, uncertain adjustment costs, and behavior preferences. Finally, it develops integrated consensus reaching frameworks that combine graph convolutional networks, uncertain asymmetric costs, and two-stage decision mechanisms for complex and large-scale decision scenarios.
Key features and benefits include:
(1) Provides a coherent framework for robust group consensus decision making under cost uncertainty and behavioral heterogeneity.
(2) Develops maximum expert consensus and minimum cost consensus models with classical, data-driven, and social network-based uncertainty structures.
(3) Integrates social trust networks, risk preferences, conflict constraints, and decision behaviors into robust consensus modeling.
(4) Offers advanced modeling tools, including quadratic adjustment costs, uncertain asymmetric costs, and graph convolutional network-based consensus mechanisms.
This book is intended for researchers, graduate students, and practitioners in decision science, management science and engineering, operations research, artificial intelligence, and risk management. It is particularly useful for readers interested in robust optimization, social network group decision making, consensus reaching processes, and intelligent decision support systems.
Shaojian Qu received the Ph.D. degree from Xi’ an Jiaotong University, China. He is currently a Longshan Scholar Distinguished Professor and doctoral supervisor with the School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, China, and also serves as the Academic Dean of the School of Economics and Management, Anhui Jianzhu University. He held postdoctoral positions at the National University of Singapore and Harbin Institute of Technology, and was selected as an Eastern Scholar Distinguished Professor in Shanghai. His research interests include big data analytics, modeling theory and methods, robust optimization, decision theory and methods, supply chain management, portfolio optimization, and game theory. His work has appeared in European Journal of Operational Research, Journal of Optimization Theory and Applications, IEEE Transactions on Industrial Informatics, China Management Science, Operations Research and Management Science, and Systems Engineering—Theory & Practice. Dr. Qu has published more than 80 papers as the first or corresponding author, including over 70 SCI-indexed papers, authored two academic monographs, led several national and provincial research projects, and serves as an editorial board member, guest editor, reviewer, and executive council member for several academic journals and societies.
Ying Ji is currently a Professor and doctoral supervisor with the School of Management, Shanghai University, Shanghai, China. She was exceptionally promoted to Professor and has been selected for the Shanghai Oriental Talent Program and the Shanghai Young Eastern Scholar Program. She was previously a Research Fellow with the Business School, National University of Singapore, an Associate Professor at Harbin Institute of Technology, and the Head of the Information Management and Information Systems discipline at University of Shanghai for Science and Technology. Her research interests include intelligent decision making, optimization methods, management science, operations management, and game theory. Her work has appeared in European Journal of Operational Research, Journal of the Operational Research Society, International Journal of Production Research, IEEE Transactions on Industrial Informatics, and Journal of Optimization Theory and Applications. Dr. Ji has published more than 70 SCI/SSCI-indexed papers as the first or corresponding author, led three projects funded by the National Natural Science Foundation of China, and participated in two key projects of the National Natural Science Foundation of China and one major project funded by the Ministry of Industry and Information Technology of China.