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.