越览（89）——精读复刻论文的应用实例的理解与计算（10）

摘要：This issue will introduce the understanding and calculating the application example of the intensively read replica paper "Emergen

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“越览（89）——精读复刻论文

《基于多粒度概率语言和双参照点的

应急决策方法》应用实例的理解与计算（10）。”

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increase Knowledge, and leave beautiful.

Dear, this is the LearingYard Academy!

Today, the editor brings the

"Yue Lan(89)—intensive reading replica paper

'Emergency decision-making method based on

multi-granularity probability language

and dual reference points

'Understanding and calculating

the application example (10)".

Welcome to visit!

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精读复刻论文《基于多粒度概率语言和双参照点的应急决策方法》应用实例的理解与计算（10）。

This issue will introduce the understanding and calculating the application example of the intensively read replica paper "Emergency decision-making method based on multi-granularity probability language and dual reference points" in terms of mind maps, intensively read content, and knowledge supplementation.

二、思维导图（Mind mapping）

三、精读内容（Intensive reading content）

上周已经计算出了案例中预估概率的损失感知权重函数。

Last week, the loss-aware Weighting function for the estimated probability in the case was calculated.

进一步地，依据式（23）确定当可行方案Xj实施时，各项关键风险因素的感知价值矩阵N。

Furthermore, according to formula (23), the perceived value matrix N of each key risk factor is determined when the feasible solution Xj is implemented.

其中nji表示当可行方案Xj实施时，应急决策组对于关键风险因素di的感知价值。

Where nji represents the perceived value of the key risk factor di by the emergency decision-making group when the feasible plan Xj is implemented.

本周将学习用代码计算出采用不同可行方案时各项关键风险因素的感知价值nji。复刻代码如下图所示：

This week, we will learn to use code to calculate the perceived value nji of each key risk factor when adopting different feasible solutions. The replica code is shown below:

X1-0.359-0.737-0.060-1.1720.6022.290X20.000-0.2291.1450.3431.2952.481X3-0.505-0.4221.0150.4180.693-2.367

四、知识补充（Knowledge supplement）

在多属性决策的文章中，对比分析是一个核心方法，能够帮助研究者从多个选项或方法中找到最优解，并揭示每种方法或属性的影响力和适用范围。具体来说，进行对比分析的原因包括以下几点：

In articles on multi-attribute decision-making, comparative analysis is a core method that can help researchers find the optimal solution from multiple options or methods and reveal the influence and scope of application of each method or attribute. Specifically, the reasons for comparative analysis include the following:

1.揭示方案差异，支持最佳选择（Reveal the differences between solutions and support the best choice）

多属性决策通常涉及多个备选方案和属性指标。对比分析能够通过权衡各方案在不同属性上的表现，突出其优劣。例如，通过比较每个方案的成本、效益、时间效率等，可以更直观地识别出最优方案或平衡各属性的次优方案，为实际决策提供依据。

Multi-attribute decision making usually involves multiple alternatives and attribute indicators. Comparative analysis can highlight the pros and cons of each option by weighing its performance on different attributes. For example, by comparing the cost, benefit, time efficiency, etc. of each option, the optimal option or the suboptimal option that balances various attributes can be more intuitively identified, providing a basis for actual decision making.

2.验证模型或方法的有效性（Verify the effectiveness of the model or method）

在研究中，往往会涉及多种决策模型（如AHP、TOPSIS、VIKOR等）。通过对比分析这些方法的结果，可以验证模型的合理性、鲁棒性和适用场景，从而为后续研究提供理论支持和实践参考。

In research, multiple decision-making models are often involved (such as AHP, TOPSIS, VIKOR, etc.). By comparing and analyzing the results of these methods, the rationality, robustness and applicable scenarios of the models can be verified, thus providing theoretical support and practical reference for subsequent research.

3.避免单一属性的片面结论（Avoid one-sided conclusions from a single attribute）

多属性决策的复杂性在于每个属性可能具有不同的权重和重要性。如果仅从单一维度分析，可能会导致偏颇的结论。对比分析通过全面评估各属性的作用，揭示其对决策结果的整体影响，避免了片面性。

The complexity of multi-attribute decision-making lies in the fact that each attribute may have different weights and importance. If only a single dimension is analyzed, it may lead to biased conclusions. Comparative analysis can avoid one-sidedness by comprehensively evaluating the role of each attribute and revealing its overall impact on the decision result.

4.优化权重分配与决策方法（Optimizing weight distribution and decision-making methods）

权重分配是多属性决策中的关键问题。通过对比不同权重分配方案或敏感性分析，可以找出最合理的权重设置方式，提升决策的科学性。同时，对比不同方法的计算结果还能帮助优化选择决策工具，增强其实际应用价值。

Weight allocation is a key issue in multi-attribute decision making. By comparing different weight allocation schemes or sensitivity analysis, we can find the most reasonable way to set weights and improve the scientific nature of decision making. At the same time, comparing the calculation results of different methods can also help optimize the selection of decision tools and enhance their practical application value.

5.提高决策透明度与可解释性（Improving transparency and explainability of decision making）

决策往往涉及多个利益相关者。对比分析能够直观展示各方案在不同属性上的表现，以及决策方法的计算逻辑。这种透明性能够减少争议，提升方案选择的说服力和公信力。

Decisions often involve multiple stakeholders. Comparative analysis can intuitively show the performance of each solution on different attributes, as well as the calculation logic of the decision-making method. This transparency can reduce disputes and enhance the persuasiveness and credibility of solution selection.

6.揭示动态性和多样性（Revealing dynamism and diversity）

在动态环境中，属性的权重或选项的优先级可能发生变化。对比分析有助于揭示方案在不同场景下的表现差异，为动态决策或调整提供支持。

In a dynamic environment, the weights of attributes or the priorities of options may change. Comparative analysis helps reveal the differences in performance of solutions in different scenarios and provides support for dynamic decision-making or adjustments.

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