Yucheng Dong – författare

This book brings together past experience, current work and promising future trends associated with distributed computing, artificial intelligence and their application in order to provide efficient solutions to real problems.

This book is intended for researchers and postgraduates who are interested in the consensus reaching process in group decision-making problems. 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.

This book proposes a novel CWW model to personalize individual semantics in linguistic decision making, based on two new concepts: numerical scale and consistency-driven methodology. The numerical scale model provides a unified framework to connect different linguistic symbolic computational models for CWW, and the consistency-driven methodology customizes individuals’ semantics to support linguistic group decision making by setting personalized numerical scales. The book is a valuable resource for researchers and postgraduates who are interested in CWW in linguistic decision making.

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.​

This book provides a comprehensive perspective on the development of distributed linguistic representations in decision making, including the taxonomy of existing distributed linguistic representations, the key elements, and the classifications.

This book investigates the DeGroot model in social network contexts, and proposes the social network DeGroot (SNDG) model.