Risk Matrix: Rating Scheme Design and Risk Aggregation (Innovation in Risk Analysis) - Tapa dura

Libro 2 de 2: Innovation in Risk Analysis

Bao, Chunbing; Wu, Dengsheng; Li, Jianping

 
9789811914799: Risk Matrix: Rating Scheme Design and Risk Aggregation (Innovation in Risk Analysis)
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ISBN 10: 9811914796 ISBN 13: 9789811914799
Editorial: Springer, 2022

Sinopsis

This book focuses on discussing the issues of rating scheme design and risk aggregation of risk matrix, which is a popular risk assessment tool in many fields. Although risk matrix is usually treated as qualitative tool, this book conducts the analysis from the quantitative perspective. The discussed content belongs to the scope of risk management, and to be more specific, it is related to quick risk assessment. This book is suitable for the researchers and practitioners related to qualitative or quick risk assessment and highly helps readers understanding how to design more convincing risk assessment tools and do more accurate risk assessment in a uncertain context.

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Acerca del autor

Chunbing Bao is an associate professor working in the Department of Information System and Supply Chain Management, Shandong University, China. He received his bachelor's degree from the University of Science and Technology of China, and he received his Ph.D. from the University of Chinese Academy of Sciences. His researches interests include risk management, supply chain management. 
Jianping Li is a distinguished professor at the University of Chinese Academy of Sciences (UCAS). He is also the executive vice dean of the School of Economics and Management at the UCAS and the executive member and secretary-general of the International Academy on Information Technology and Quantitative Management (IAITQM). His research interests include risk management and big data analysis. He has published more than 130 papers and 6 monographs, including Risk Analysis, Annals of Tourism Research, and European Journal of Operational Research. He has been awarded "2020Elsevier Highly Cited Chinese Researchers", "China Youth Science and Technology Award", "National Outstanding Science and Technology Worker of China", "Excellent Tutor Award of Chinese Academy of Sciences", and won 2 first prizes and 4 second prizes of provincial and ministerial Natural Science/Technology Progress Awards in China. 
Dengsheng Wu is an associate professor at the Institutes of Science and Development Chinese Academy of Sciences. He is a member of the Youth Innovation Promotion Association of Chinese Academy of Sciences and severs as the academic editor of the Innovation, associate editor of the Chinese Journal of Management Science. He has published 50 articles, among which more than 20 articles are indexed by the FMS journal rating guide, such as the important journal Risk Analysis, European Journal of Operational Research. His researches interests include risk management, Scientometrics.

De la contraportada

This book focuses on discussing the issues of rating scheme design and risk aggregation of risk matrix, which is a popular risk assessment tool in many fields. Although risk matrix is usually treated as qualitative tool, this book conducts the analysis from the quantitative perspective. The discussed content belongs to the scope of risk management, and to be more specific, it is related to quick risk assessment. This book is suitable for the researchers and practitioners related to qualitative or quick risk assessment and highly helps readers understanding how to design more convincing risk assessment tools and do more accurate risk assessment in a uncertain context.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2022
Idioma
Inglés
ISBN 10 
9811914796
ISBN 13 
9789811914799
Encuadernación
Tapa dura
Número de edición
1
Número de páginas
184
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Otras ediciones populares con el mismo título

9789811914829: Risk Matrix: Rating Scheme Design and Risk Aggregation (Innovation in Risk Analysis)

Edición Destacada

ISBN 10:  9811914826 ISBN 13:  9789811914829
Editorial: Springer, 2023
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