越览（104）——精读硕士论文的1.4研究动机

摘要：This issue of tweets will introduce the research motivation of the master's thesis "Research on Multi-Attribute Fuzzy Decision-mak

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“越览（104）——精读硕士论文

《基于不完备信息系统的多属性模糊决策方法研究》

的1.4研究动机”。

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Today, the editor brings you

"Yuelan (104): Intensive reading of the master's thesis

"1.4 Research motivation of

multi-attribute fuzzy decision-making

methods based on incomplete

information systems"".

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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硕士论文《基于不完备信息系统的多属性模糊决策方法研究》的研究动机。

This issue of tweets will introduce the research motivation of the master's thesis "Research on Multi-Attribute Fuzzy Decision-making Method Based on Incomplete Information System" from three aspects: mind map, intensive reading content, and knowledge supplement.

二、思维导图（Mind mapping）

三、精读内容（Intensive reading content）

本文的研究动机在于解决当前不完备信息系统决策方法的局限性。现有的研究大多依赖于传统的三支决策框架，主要通过备选方案分类来进行决策，但难以有效应对有排序需求的多属性决策问题。现有的处理不完备信息的方法（如删除法、数据补全法和模型扩展法）在某些情况下会影响原始数据的可靠性，且难以实现全面排序。模糊多属性决策方法虽然适用于排序，但在信息缺失和属性权重确定上存在挑战。

The motivation of this study is to address the limitations of current decision-making methods in incomplete information systems. Most existing studies rely on the traditional three-way decision framework, which mainly makes decisions by classifying alternatives, but it is difficult to effectively deal with multi-attribute decision-making problems with sorting requirements. Existing methods for dealing with incomplete information (such as deletion, data completion, and model expansion) will affect the reliability of the original data in some cases, and it is difficult to achieve comprehensive sorting. Although fuzzy multi-attribute decision-making methods are suitable for sorting, there are challenges in missing information and determining attribute weights.

为了解决这些问题，本文提出结合模糊模式识别的特点，利用不完整信息来灵活确定标准属性集，从而改进决策过程。此外，传统决策方法假设决策者为“完全理性人”，未能充分考虑决策者的意图和需求，而基于前景理论的决策方法能够更好地反映决策者的参与度和意图。因此，本文希望通过基于前景理论的多属性模糊模式决策方法，为不完备信息系统的决策提供更加全面、灵活的解决方案。

In order to solve these problems, this paper proposes to combine the characteristics of fuzzy pattern recognition and use incomplete information to flexibly determine the standard attribute set, thereby improving the decision-making process. In addition, the traditional decision-making method assumes that the decision maker is a "completely rational person" and fails to fully consider the decision maker's intentions and needs. The decision-making method based on prospect theory can better reflect the decision maker's participation and intentions. Therefore, this paper hopes to provide a more comprehensive and flexible solution for decision-making in incomplete information systems through a multi-attribute fuzzy pattern decision-making method based on prospect theory.

四、知识补充（Knowledge supplement）

不完备信息的处理方法主要有以下几种：

There are several ways to deal with incomplete information:

1. 直接删除法：这种方法是直接删除缺失信息的样本或属性。虽然操作简单，但会导致数据量的减少，可能会影响决策的可靠性，特别是当缺失数据量较大时，可能导致分析结果的偏差。

1. Direct deletion method: This method directly deletes samples or attributes with missing information. Although the operation is simple, it will lead to a reduction in the amount of data, which may affect the reliability of decision-making, especially when the amount of missing data is large, which may lead to deviations in analysis results.

2. 数据补全法：数据补全法通过推测或估计缺失数据的值来填补信息空缺。常用的补全方法包括：

2. Data completion method: Data completion method fills in information gaps by inferring or estimating the value of missing data. Commonly used completion methods include:

(1) 均值填充：用属性的均值代替缺失值。

(1) Mean filling: replace missing values with the mean of the attribute.

(2) 回归填充：根据已有数据构建回归模型来预测缺失数据。

(2) Regression filling: Build a regression model based on existing data to predict missing data.

(3) 插值法：根据相邻的数据点进行插值填充。该方法在填补缺失数据时，能够保持数据的完整性，但如果补全不当，可能会引入误差，影响决策结果。

(3) Interpolation method: interpolation is performed based on adjacent data points. This method can maintain the integrity of the data when filling in missing data, but if the filling is not done properly, it may introduce errors and affect the decision-making results.

3. 模型扩展法：模型扩展法结合现有的信息和模型，推导出缺失信息的可能性范围。这种方法较为复杂，通常需要较强的数学和统计建模支持。模型扩展法可以利用已知信息预测缺失信息的分布和趋势，从而提高决策的准确性。

3. Model extension method: The model extension method combines existing information and models to deduce the range of possibilities for missing information. This method is more complex and usually requires strong mathematical and statistical modeling support. The model extension method can use known information to predict the distribution and trend of missing information, thereby improving the accuracy of decision-making.

此外，模糊多属性决策方法和基于粗糙集理论的粒计算方法也被广泛应用于处理不完备信息，尤其是在解决属性约简和决策规则获取等问题时具有优势，但在多属性决策中处理信息缺失和属性权重方面仍存在挑战。

In addition, fuzzy multi-attribute decision-making methods and granular computing methods based on rough set theory are also widely used to deal with incomplete information, especially in solving problems such as attribute simplification and decision rule acquisition. However, there are still challenges in dealing with missing information and attribute weights in multi-attribute decision-making.

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