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今天小编为大家带来“越览（64）——精读博士论文：
《基于多粒度犹豫模糊语言信息的
多属性群决策方法研究》的1绪论（2）：
国内外研究现状、研究内容和论文结构”。
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Dear, this is LearningYard Academy!
Today, the editor brings you
"Yue Lan (64)：the introduction (2):
Current status of research at home and abroad,
research content and
paper structure of the doctoral dissertation
'Research on multi-attribute group decision method
based on multi-granularity
hesitant fuzzy language information'".
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一、内容摘要（Summary of content）
本期推文将从思维导图、精读内容、知识补充三个方面介绍博士论文《基于多粒度犹豫模糊语言信息的多属性群决策方法研究》的绪论（2）：国内外研究现状、研究内容和论文结构。
This tweet will introduce the introduction (2): Current status of research at home and abroad, research content and paper structure "Research on multi-attribute group decision method based on multi-granularity hesitant fuzzy language information" from three aspects: mind mapping, intensive reading content, and knowledge supplementation.
二、思维导图（Mind mapping）
三、精读内容（Intensive reading content）
(一) 国内外研究现状（Research status at home and abroad）
本次推文就基于分布式语言信息的决策方法和大群体决策方法这两个方面来介绍。
This tweet will introduce two aspects: decision-making methods based on distributed language information and large group decision-making methods.
1. 基于分布式语言信息的决策方法（Decision-making method based on distributed language information）
本部分总结了国内外学者在分布式语言信息及其在多属性群决策中的研究进展，涵盖了分布式语言信息的多种形式与定义、基于此的决策方法及一致性与共识模型的提出，并将其应用于多个实际决策领域。此外，研究还探讨了犹豫模糊语言术语集与分布式语言信息的关系，并综述了该领域的应用与挑战，指出其在增强专家偏好表达灵活性及群决策问题解决中的重要性。
This section summarizes the research progress of domestic and foreign scholars on distributed language information and its application in multi-attribute group decision-making, covering the various forms and definitions of distributed language information, decision-making methods based on it, and the proposal of consistency and consensus models, and applies them to multiple practical decision-making fields. In addition, the study also explores the relationship between hesitant fuzzy language term sets and distributed language information, and summarizes the applications and challenges in this field, pointing out its importance in enhancing the flexibility of expert preference expression and solving group decision-making problems.
2. 大群体决策方法（Large group decision making methods）
传统群体决策中，参与规模较小。然而，随着社会经济的发展，决策问题日益复杂。在诸如重大灾害应急决策、基础设施工程决策、精准扶贫、移民安置等问题中，参与决策的群体规模不断扩大，可能涉及数十至数千人，且成员背景多样，代表不同利益群体。近年来，信息通信技术的进步进一步推动了大群体决策的发展，使更多个体可异时异地参与决策。
In traditional group decision-making, the scale of participation is relatively small. However, with the development of social economy, decision-making problems are becoming increasingly complex. In issues such as major disaster emergency decision-making, infrastructure project decision-making, targeted poverty alleviation, and resettlement of immigrants, the scale of the group participating in decision-making continues to expand, and may involve dozens to thousands of people, with diverse backgrounds and representing different interest groups. In recent years, the advancement of information and communication technology has further promoted the development of large-group decision-making, allowing more individuals to participate in decision-making at different times and places.
通常，参与人数超过20人的决策问题可视为大群体决策。自2005年以来，大群体决策在学术界受到了广泛关注并取得了诸多成果。陈晓红院士团队为国内该领域的先行者，提出了改进聚类算法、多属性决策方法等，应用于灾害管理和应急决策。其他学者也对大群体决策模型进行了深入研究，如区间值直觉模糊数、主成分分析等方法被提出并应用于复杂多属性决策中。
Generally, decision-making problems involving more than 20 participants can be considered as large group decision-making. Since 2005, large group decision-making has received widespread attention in the academic community and has achieved many results. Academician Chen Xiaohong's team is a pioneer in this field in China. They have proposed improved clustering algorithms and multi-attribute decision-making methods, which are applied to disaster management and emergency decision-making. Other scholars have also conducted in-depth research on large group decision-making models. For example, interval-valued intuitionistic fuzzy numbers and principal component analysis have been proposed and applied to complex multi-attribute decision-making.
近年来，学者们还对大群体共识模型进行了研究，提出了基于模糊聚类、专家权重动态调整等方法，用于管理非合作行为并提升共识效率。
In recent years, scholars have also studied large group consensus models and proposed methods based on fuzzy clustering and dynamic adjustment of expert weights to manage non-cooperative behaviors and improve consensus efficiency.
3. 已有研究的不足（Deficiencies in existing research）
国内外关于语言型群决策和大群体决策的研究取得了显著进展，尤其是在基于犹豫模糊语言信息的多属性群决策方面。然而，仍存在以下问题：
Domestic and foreign research on language-based group decision-making and large group decision-making have made significant progress, especially in multi-attribute group decision-making based on hesitant fuzzy linguistic information. However, the following problems still exist:
多粒度与非平衡犹豫模糊语言信息研究薄弱：现有研究多集中于平衡术语集，而忽略了专家因背景差异可能使用不同粒度或非平衡术语集的情况。
Research on multi-granularity and unbalanced hesitant fuzzy language information is weak: existing research mostly focuses on balanced term sets, while ignoring the situation that experts may use different granularity or unbalanced term sets due to different backgrounds.
共识模型有待完善：现有模型在度量共识水平时，主要针对单一属性，忽视了综合评价的共识。此外，反馈调整建议的可解释性不足，特别是对于多粒度和非平衡术语集的适用性较弱。
The consensus model needs to be improved: When measuring the consensus level, the existing models mainly focus on a single attribute and ignore the consensus of comprehensive evaluation. In addition, the interpretability of feedback adjustment suggestions is insufficient, especially for multi-granularity and unbalanced term sets.
方案排序方法不足：现有方法易导致信息损失，且对专家的心理行为（如参照依赖、损失规避）考虑较少。
Insufficient methods for ranking options: Existing methods are prone to information loss and give little consideration to the psychological behavior of experts (e.g., reference dependence and loss aversion).
大群体决策模型研究缺乏：在大群体决策中，专家使用不同类型犹豫模糊语言信息的情况更加普遍，现有方法难以有效集成专家评价并提供可解释性决策结果。
There is a lack of research on large group decision-making models: In large group decision-making, it is more common for experts to use different types of hesitant and ambiguous language information. Existing methods find it difficult to effectively integrate expert evaluations and provide explainable decision results.
(二) 研究内容（Research content）
作者针对多粒度犹豫模糊语言型多属性群决策问题，提出了三种不同情境下的决策模型与方法，旨在解决复杂决策问题并提供支持。主要研究内容如下：
The author proposes three decision-making models and methods in different situations for multi-granularity hesitant fuzzy linguistic multi-attribute group decision-making problems, aiming to solve complex decision-making problems and provide support. The main research contents are as follows:
1. 基于多粒度平衡犹豫模糊语言的多属性群决策方法：提出共识达成模型和排序方法，综合考虑专家与属性权重，解决反馈调整过度及信息损失问题。
1. Multi-attribute group decision-making method based on multi-granularity balanced hesitant fuzzy language: A consensus reaching model and ranking method are proposed, which comprehensively considers the weights of experts and attributes to solve the problems of excessive feedback adjustment and information loss.
2. 基于多粒度非平衡犹豫模糊语言的多属性群决策方法：提出共识反馈机制和基于专家心理行为的排序方法，解决调整成本高、可解释性差及参照依赖问题。
2. Multi-attribute group decision-making method based on multi-granularity non-equilibrium hesitant fuzzy language: A consensus feedback mechanism and a ranking method based on expert psychological behavior are proposed to solve the problems of high adjustment cost, poor interpretability and reference dependence.
3. 基于多粒度犹豫模糊语言的多属性大群决策方法：针对大群体决策问题，提出转化算法、专家聚类和共识达成模型，解决大群决策中的可解释性及多类型语言术语集问题。
3. Multi-attribute large group decision-making method based on multi-granularity hesitant fuzzy language: For large group decision-making problems, a transformation algorithm, expert clustering and consensus reaching model are proposed to solve the problems of explainability and multi-type language terminology sets in large group decision-making.
(三) 论文结构（Paper Structure）
本文研究了多粒度犹豫模糊语言型多属性群决策问题，并提出了相应的决策方法。研究内容包括理论基础、方法模型、算例分析和结果验证，具体结构如下：
This paper studies the multi-granularity hesitant fuzzy linguistic multi-attribute group decision-making problem and proposes a corresponding decision-making method. The research content includes theoretical basis, method model, example analysis and result verification. The specific structure is as follows:
四、知识补充——Uninorm算子（Knowledge Supplement — Uninorm Operator）
Uninorm算子是一类广泛用于模糊集合理论和多准则决策的聚合算子，它能够综合处理输入值之间的相互作用。Uninorm算子在逻辑系统中具有广泛应用，特别是在模糊逻辑、模糊推理、多属性决策等领域。其主要特点是结合了t-范数（T-norm）和t-共范数（T-conorm）的特性，具有更加灵活的运算能力。
Uninorm operator is a kind of aggregation operator widely used in fuzzy set theory and multi-criteria decision making. It can comprehensively handle the interaction between input values. Uninorm operator is widely used in logic systems, especially in fuzzy logic, fuzzy reasoning, multi-attribute decision making and other fields. Its main feature is that it combines the characteristics of t-norm (T-norm) and t-co-norm (T-conorm), and has more flexible computing capabilities.
(一) Uninorm算子的定义（Definition of Uninorm Operator）
Uninorm算子是一种二元运算，它将两个输入值映射到[0,1]区间，通常定义如下：
The Uninorm operator is a binary operation that maps two input values to the interval [0,1] and is usually defined as follows:
Uninorm算子U(a,b)的输入值a和b都在[0,1]区间内。Uninorm算子由一个中间元素e∈(0,1)控制，该元素决定了t-范数和t-共范数的作用域。这个元素e称为中性元，即当输入值之一等于e时，Uninorm的输出就是另一个输入值的值：
The input values a and b of the Uninorm operator U(a,b) are both in the interval [0,1]. The Uninorm operator is controlled by an intermediate element e∈(0,1) that determines the scope of the t-norm and t-co-norm. This element e is called a neutral element, that is, when one of the input values is equal to e, the output of the Uninorm is the value of the other input value:
U(a,e)=a和U(e,b)=b
Uninorm的计算规则可以分为三种情况：
Uninorm calculation rules can be divided into three cases:
1. 当两个输入值都小于中性元e，即a,b<e，Uninorm的运算与t-范数类似，表现出“与”的逻辑作用，通常通过最小化函数来计算。
1. When both input values are less than the neutral element e, that is, a, b < e, the operation of Uninorm is similar to the t-norm, showing the logical effect of "and", usually calculated by minimizing the function.
2. 当两个输入值都大于中性元e，即a,b>e，Uninorm的运算与t-共范数类似，表现出“或”的逻辑作用，通常通过最大化函数来计算。
2. When both input values are greater than the neutral element e, that is, a, b>e, the operation of Uninorm is similar to that of t-co-norm, showing the logical effect of "or", and is usually calculated by maximizing the function.
3. 当输入值a和b分别处于中性元的两侧，即a<e≤b，或b<e≤a，Uninorm的值会直接等于中性元e。
3. When the input values a and b are on both sides of the neutral element, that is, a<e≤b, or b<e≤a, the value of Uninorm will be directly equal to the neutral element e.
(二) 形式化表达（Formal expression）
一个典型的Uninorm算子可以这样表示：
A typical Uninorm operator can be expressed as follows:
其中， T(a,b)为t-范数， S(a,b)为t-共范数，e为中性元。
Among them, T(a,b) is the t-norm, S(a,b) is the t-co-norm, and e is the neutral element.
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参考文献：于文玉. 基于多粒度犹豫模糊语言信息的多属性群决策方法研究 [D]. 大连理工大学, 2021.
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