Distributed Linguistic Representations and Decision Making (Uncertainty and Operations Research) - Hardcover

Wu, Yuzhu; Dong, Yucheng

Hardcover

ISBN 10: 9819649358 ISBN 13: 9789819649358

Publisher: Springer, 2025

Synopsis

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. It systematically investigates the formation of distributed linguistic representations and the methodology for the distributed linguistic information transformations and unifications, as well as the fusion and consensus reaching. This book studies the distributed linguistic information in MADM and the PIS-based applications of distributed linguistic information processing in decision making. Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. This book is written for researchers and postgraduates interested in linguistic decision making. Readers will find out lots of clear cues for distributed linguistic representations and distributed linguistic methodology to support decision making.

 

About this title

About the Author

Yuzhu Wu received the M.S. degree and the Ph.D. degree in management science and engineering from Sichuan University, Chengdu, China, in 2016 and 2019, respectively. She is currently an associate professor at the School of Business Administration, Research Institute of Big Data, Southwestern University of Finance and Economics, Chengdu, China. She has published in several journals and conference proceedings, including IEEE Transactions on Cybernetics, Information Fusion, Knowledge-Based Systems, and Computer & Industrial Engineering. Her research interests include decision making, information fusion, and computing with words.

 

Yucheng Dong received the B.S. and M.S. degrees in mathematics from Chongqing University, Chongqing, China, in 2002 and 2004, respectively, and the Ph.D. degree in management from Xi’an Jiaotong University, Xi’an, China, in 2008. He is currently a professor with the Business School, Sichuan University, Chengdu, China. He has authored or co-authored more than 200 international journal papers in some refereed journals. He won the 2021 Clemen-Kleinmuntz Decision Analysis Best Paper Award (INFORMS) and 2022 IEEE TFS Outstanding Paper Award (IEEE CIS). His current research interests include decision analysis and human dynamics. Prof. Dong is an area editor/associate editor of Computers and Industrial Engineering, Group Decision and Negotiation, IEEE Transactions on Artificial Intelligence, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and Information Fusion. He has been identified by Clarivate as a Highly Cited Researcher in the field of computer science.

From the Back Cover

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. It systematically investigates the formation of distributed linguistic representations and the methodology for the distributed linguistic information transformations and unifications, as well as the fusion and consensus reaching. This book studies the distributed linguistic information in MADM and the PIS-based applications of distributed linguistic information processing in decision making. Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. This book is written for researchers and postgraduates interested in linguistic decision making. Readers will find out lots of clear cues for distributed linguistic representations and distributed linguistic methodology to support decision making.

 

Edition details

• Publisher: Springer
• Publication date: 2025
• Language: English
• ISBN 10: 9819649358
• ISBN 13: 9789819649358
• Binding: Hardcover
• Number of pages: 217