越览（86）——精读复刻论文的应用实例的理解与计算（9）

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（86）——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（9）。”

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increase Knowledge, and leave beautiful.

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Today, the editor brings the

"Yue Lan(86)—intensive reading replica paper

'Emergency decision-making method based on

multi-granularity probability language

and dual reference points

'Understanding and calculating

the application example (9)".

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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（9）。

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收益感知权重函数。

The payoff perception weighting function for the estimated probability in the case was calculated last week.

本文中当可行方案 Xj 时，面对情景 q 在关键风险因素 di 上预估概率的损失感知权重函数为：

In this paper, when the feasible scheme Xj, the loss perception weight function of the estimated probability on the key risk factor di for scenario q is:

Θ体现应急决策组在感知权重函数中对于收益和损失的敏感程度，不失一般性，借鉴文献，有Θ=0.69。

It reflects the sensitivity of the emergency decision-making group to gains and losses in the perceived weight function, without losing generality.

损失感知权重函数具体指的是，在决策过程中，人们对损失的敏感度往往高于对等量收益的敏感度。换句话说，损失带来的痛苦通常大于相同金额获得所带来的快乐。这种现象被称为“损失厌恶”。

The loss perception weighting function specifically refers to the fact that people tend to be more sensitive to losses than to equivalent gains in the decision-making process. In other words, the pain of losing is usually greater than the pleasure of gaining the same amount. This phenomenon is called "loss aversion".

在数学模型中，损失感知权重函数通常是对概率进行非线性转换的一个函数，它反映了人们对于可能发生的收益或损失赋予的心理权重。这个函数不是基于实际的货币价值，而是基于个体对于潜在结果的情感反应。

In mathematical models, the loss perception weighting function is usually a function that performs a non-linear transformation of probabilities. It reflects the psychological weight that people place on possible gains or losses. This function is not based on actual monetary value, but on an individual's emotional response to the potential outcome.

案例中计算预估概率的损失感知权重过程的复刻代码如下：

The code for the loss perception weighting process for calculating the estimated probability in this case is as follows:

The running result is as follows:

四、知识补充（Knowledge supplement）

TOPSIS（Technique for Order Preference by Similarity to an Ideal Solution，逼近理想解排序法）是一种常用的多准则决策分析方法，最早由 Hwang 和 Yoon 于1981年提出。它是一种基于几何距离的决策方法，用于从多个候选方案中选出最优或对方案进行排序。

TOPSIS is a commonly used multi-criteria decision analysis method, first proposed by Hwang and Yoon in 1981. It is a geometric distance-based decision-making method for selecting the best from multiple candidate solutions or ranking the solutions.

（一）核心思想（Core idea）

TOPSIS 的基本思想是：最优解应与理想解（最佳解）距离最小，同时与负理想解（最差解）距离最大。

The basic idea of TOPSIS is that the optimal solution should have the smallest distance from the ideal solution and the largest distance from the Negative ideal solution.

1. 理想解：每个准则下的最佳值，表示最优目标。

1. Ideal solution: The optimal value under each criterion represents the optimal goal.

2. 负理想解：每个准则下的最差值，表示最劣目标。

2. Negative ideal solution: The worst value under each criterion represents the worst target.

通过比较候选方案到这两种解的几何距离，可以评估候选方案的相对优劣。

By comparing the geometric distances of the candidate schemes to the two solutions, the relative merits and demerits of the candidate schemes can be evaluated.

（二）TOPSIS 的步骤（The steps of TOPSIS）

1. 构造决策矩阵（Construct decision matrix）

With m alternatives and n evaluation indicators, a decision matrix is constructed: