基于语言信息的决策理论与方法(英文版)

    书籍
    数学书籍
Linguistic Decision Making: Theory and Methods, is the first monograph which mainly deals with an interdisciplinary subject of computing with words,information fusion and decision analysis. It provides a thorough and systematic introduction to the linguistic aggregation operators, linguistic preference relations,and various models for and approaches to multi-attribute decision making with linguistic information. It also offers various practical examples with tables and figures to illustrate the theory and methods discussed. Researchers and professionals engaged in the relevant fields will find it an useful reference book.

Professor Zeshui Xu, senior member of the IEEE, works at the PLA University of Science and Techology, China.

《基于语言信息的决策理论与方法(英文版)》
Chapter 1 Linguistic Evaluation Scales
1.1 Additive Linguistic Evaluation Scales
1.2 Multiplicative Linguistic Evaluation Scales
References
Chapter 2 Linguistic Aggregation Operators
2.1 Linguistic Aggregation Operators Based on Linear Ordering
2.2 Linguistic Aggregation Operators Based on Extension Principle and Symbols
2.3 Linguistic Aggregation Operators Based on Linguistic 2-tuples
2.4 Linguistic Aggregation Operators Computing with Words Directly
2.4.1 Linguistic Averaging Operator
2.4.2 Linguistic Geometric Operators
References
Chapter 3 Linguistic Preference Relations
3.1 Additive Linguistic Preference Relations
3.2 Incomplete Additive Linguistic Preference Relations
3.3 Dynamic Additive Linguistic Preference Relations
3.4 Multiplicative Linguistic Preference Relations
3.5 Incomplete Multiplicative Linguistic Preference Relations
3.6 Dynamic Multiplicative Linguistic Preference Relations

　　The complexity and uncertainty of objective thing and the fuzziness of human thought result in decision making with linguistic information in the fields of society, economy,medicine, management and military affairs, etc., such as personnel evaluation, military system performance evaluation, online auctions, supply chain management, venture capital and medical diagnostics.
　　In real-life, there are many situations, such as evaluating university faculty for tenure and promotion and evaluating the performance of different kinds of stocks and bonds, in which the information cannot be assessed precisely in numerical values but may be in linguistic variables. That is, variables whose values are not numbers but words or sentences in a natural or artificial language. For example, when evaluating the "comfort" or "design" of a car, linguistic labels like "good", "fair" and "poor" are usually used, and evaluating a car's speed, linguistic labels like "very fast", "fast" and "slow" can be used. Therefore, how to make a scientific decision with linguistic information is an interesting and important research topic that has been attracting more and more attention in recent years.
　　To date, a lot of methods have been proposed for dealing with linguistic informa-tion. These methods are mainly as follows:
　　(1) The methods based on extension principle, which make operations on the fuzzy numbers that support the semantics of the linguistic labels.
　　(2) The methods based on symbols, which make computations on the indexes of the linguistic terms.
　　Both the above methods develop some approximation processes to express the results in the initial expression domains, which produce the consequent loss of infor-mation and hence the lack of precision.
　　(3) The methods based on fuzzy linguistic representation model, which represent the linguistic information with a pair of values called 2-tuple, composed by a lin-guistic term and a value of the symbolic translation. Together with the model, the methods also give some computational techniques to deal with the 2-tuples without loss of information. But the model needs some transformation between a counting of information and the linguistic 2-tuple by a function in the aggregation process, and thus, the model is somewhat cumbersome in representation.
　　(4) The methods which compute with words directly.
　　Compared with the methods (1)～(3), the methods (4) can not only avoid los-lng any linguistic information, but also are straightforward and very convenient in calculation, and thus, are more practical in actual applications.
　　In recent years, the author has made an in-depth and systematical research on the methods (4) and their applications. The research results mainly include linguistic evaluation scales, linguistic aggregation operators, uncertain linguistic aggregation op-erators, dynamic linguistic aggregation operators, the priority theory and methods of linguistic preference relations, uncertain linguistic preference relations and incomplete linguistic preference relations, interactive approach to linguistic multi-attribute deci-sion making, linguistic multi-attribute group decision making methods, dynamic lin-guistic multi-attribute decision making methods, uncertain linguistic multi-attribute decision making methods, and their applications in solving a variety of practical prob-lems, such as the partner selection of supply chain management, personnel appraisal,investment decision making, military system efficiency dynamic evaluation, venture capital project evaluation, and enterprise technology innovation capacity evaluation,etc. This book will give a thorough and comprehensive introduction to these results,which mainly consists of the following parts:
　　The preface to this book gives a brief background introduction to the current study on the theory and methods of linguistic decision making, and summarizes the main contents and structure.
　　Chapter 1 mainly introduces the basis of linguistic decision making-Linguistic evaluation scales. Linguistic evaluation scales are classified into two types: additive linguistic evaluation scales and multiplicative linguistic evaluation scales. The unbal-anced additive linguistic label sets and unbalanced multiplicative linguistic label sets are highlighted.
　　Chapter 2 introduces the aggregation techniques for linguistic information. A comprehensive survey of the existing main linguistic aggregation operators is provided.
　　 Chapter 3 mainly introduces the concepts of linguistic preference relations, uncer-tain linguistic preference relations, incomplete linguistic preference relations, consis-tent linguistic preference relations, acceptable linguistic preference relations, and their desirable properties. Then the decision making approaches based on these linguistic preference relations are also overviewed.
　　Chapter 4 mainly introduces the approaches to linguistic multi-attribute decision making. Based on a variety of linguistic aggregation operators, such as the linguistic weighted averaging operator, dynamic linguistic weighted averaging operator, linguis-tic weighted geometric operator and dynamic linguistic weighted geometric operator,etc., a series of methods and models for multi-attribute decision making under linguis-tic environments are established, including the maximizing deviation procedure, ideal point-based model, goal programming model, interactive decision making approach,and multi-period multi-attribute decision making method, etc. Furthermore, most of these results are extended to accommodate multi-attribute decision making under uncertain linguistic environments.
　　This book is suitable for the engineers, technicians and researchers in the fields of fuzzy mathematics, operations research, information science, management science and systems engineering, etc. It can also be used as a textbook for the senior un-dergraduate and graduate students in the relevant professional institutions of higher learning.
　　Zeshui Xu
　　Nanjing
　　December, 2011