Large Scale Group Decision Making

This book investigates in detail large-scale group decision-making (LSGDM) problem, which has gradually evolved from the traditional group decision-making problem and has attracted more and more attention in the age of big data. Pursuing a holistic approach, the book establishes a fundamental framework for LSGDM with uncertain and behavioral considerations. To address the behavioral uncertainty and complexity of large groups of decision-makers, this book mainly focuses on new solutions of LSGDM problems using the interval type-2 fuzzy uncertainty theory and social network analysis techniques, including the exploration of uncertain clustering analysis, the consideration of social relationships, especially trust relationships, the construction of consensus evolution networks, etc. The book is intended for researchers and postgraduates who are interested in complex group decision-making in the new media era. Authors also investigate the similar features between LSGDM problems and group recommendations to study the applications of LSGDM methods. After reading this book, readers will have a new understanding of the LSGDM study under the real complicated context.

This book explores clustering operations in the context of social networks and consensus-reaching paths that take into account non-cooperative behaviors. This book focuses on the two key issues in large-scale group decision-making: clustering and consensus building. Clustering aims to reduce the dimension of a large group. Consensus reaching requires that the divergent individual opinions of the decision makers converge to the group opinion. This book emphasizes the similarity of opinions and social relationships as important measurement attributes of clustering, which makes it different from traditional clustering methods with single attribute to divide the original large group without requiring a combination of the above two attributes. The proposed consensus models focus on the treatment of non-cooperative behaviors in the consensus-reaching process and explores the influence of trust loss on the consensus-reaching process.The logic behind is as follows: firstly, a clustering algorithm is adopted to reduce the dimension of decision-makers, and then, based on the clusters’ opinions obtained, a consensus-reaching process is carried out to obtain a decision result acceptable to the majority of decision-makers. Graduates and researchers in the fields of management science, computer science, information management, engineering technology, etc., who are interested in large-scale group decision-making and consensus building are potential audience of this book. It helps readers to have a deeper and more comprehensive understanding of clustering analysis and consensus building in large-scale group decision-making.

This book focuses on the following three key topics in social network large-scale decision-making: structure-heterogeneous information fusion, clustering analysis with multiple measurement attributes, and consensus building considering trust loss. To address the aggregation and distance measurement of structure-heterogeneous evaluation information, we propose a fusion method based on trust and behavior analysis. Then, two clustering algorithms are put forward, including trust Cop-K-means clustering algorithm and compatibility distance-oriented off-center clustering algorithm. The above clustering algorithms emphasize the similarity of opinions and social relationships as important measurement attributes of clustering. Finally, this book explores the impact of trust loss originating from social relationships on the CRP and develops two consensus-reaching models, namely the improved minimum-cost consensus model that takes into account voluntary trust loss and the punishment-driven consensus-reaching model. Some case studies, a large number of numerical experiments, and comparative analyses are provided in this book to demonstrate the characteristics and advantages of the proposed methods and models. The authors encourage researchers, students, and enterprises engaged in social network analysis, group decision-making, multi-agent collaborative decision-making, and large-scale data processing to pay attention to the proposals presented in this book. After reading this book, the authors expect readers to have a deeper and more comprehensive understanding of social network large-scale decision-making. Inorder to make it more accurate for readers to understand the methods and models presented in this book, the authors strongly recommend that potential readers have a good research foundation in fuzzy soft computing, traditional clustering algorithms, basic mathematics knowledge, and other related preliminaries.

This SpringerBrief provides a pioneering, central point of reference for the interested reader in Large Group Decision Making trends such as consensus support, fusion and weighting of relevant decision information, subgroup clustering, behavior management, and implementation of decision support systems, among others. Based on the challenges and difficulties found in classical approaches to handle large decision groups, the principles, families of techniques, and newly related disciplines to Large-Group Decision Making (such as Data Science, Artificial Intelligence, Social Network Analysis, Opinion Dynamics, Behavioral and Cognitive Sciences), are discussed. Real-world applications and future directions of research on this novel topic are likewise highlighted.

Publication of the Handbook of Group Decision and Negotiation marks a milestone in the evolution of the group decision and negotiation (GDN) eld. On this occasion, editors Colin Eden and Marc Kilgour asked me to write a brief history of the eld to provide background and context for the volume. They said that I am in a good position to do so: Actively involved in creating the GDN Section and serving as its chair; founding and leading the GDN journal, Group Decision and Negotiation as editor-in-chief, and the book series, “Advances in Group Decision and Negotiation” as editor; and serving as general chair of the GDN annual meetings. I accepted their invitation to write a brief history. In 1989 what is now the Institute for Operations Research and the Management Sciences (INFORMS) established its Section on Group Decision and Negotiation. The journal Group Decision and Negotiation was founded in 1992, published by Springer in cooperation with INFORMS and the GDN Section. In 2003, as an ext- sion of the journal, the Springer book series, “Advances in Group Decision and Negotiation” was inaugurated.

Tackling the question of how to effectively aggregate uncertain preference information in multiple structures given by decision-making groups, Theory and Approaches of Unascertained Group Decision-Making focuses on group aggregation methods based on uncertainty preference information. It expresses the complexity existing in each group decision-maki

Optimization is a field important in its own right but is also integral to numerous applied sciences, including operations research, management science, economics, finance and all branches of mathematics-oriented engineering. Constrained optimization models are one of the most widely used mathematical models in operations research and management science. This book gives a modern and well-balanced presentation of the subject, focusing on theory but also including algorithims and examples from various real-world applications. Detailed examples and counter-examples are provided--as are exercises, solutions and helpful hints, and Matlab/Maple supplements.

This book is intended for researchers and postgraduates who are interested in the consensus reaching process in group decision-making problems. It puts forward new optimization-based decision support approaches to help decision-makers find roadmaps to consensus with minimum adjustments. Simulation experiments and comparison analysis are subsequently conducted to assess the validity of the proposal. After reading this book, readers will possess a number of valuable tools for building consensus with minimum adjustments in the context of group decision-making. Further, the proposed approach can effectively reduce costs in consensus building.​