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（80）——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（7）。”

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Dear, this is the LearingYard Academy!

Today, the editor brings the

"Yue Lan(80)—intensive reading replica paper

'Emergency decision-making method based on

multi-granularity probability language

and dual reference points

'Understanding and calculating

the application example (7)".

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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（7）。

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）

上周已经计算出了效益型风险因素上预估效果的感知价值计算公式本文案例中概率密度函数。

Last week, we calculated the perceived value formula for predicting the effect of the beneficial risk factor the probability density function in this case.

本周将用代码实现感知价值公式的复刻。确定不同可行方案实施时，面对不同情景在各项关键风险因素上的偏差值，考虑到本案例将所有指标已转化为效益型风险因素，依据式（18）计算不同可行方案实施时，面对不同情景在各项关键风险因素上的感知价值。案例中效益型风险因素上预估效果的感知价值计算公式为下图所示：

This week, a replica of the perceived value formula will be implemented with code. To determine the deviation values of different scenarios on various key risk factors when implementing different feasible solutions, considering that all indicators have been converted into utility-type risk factors in this case, according to formula (18) to calculate the perceived value of different scenarios on various key risk factors when implementing different feasible solutions. The perceived value calculation formula of the estimated effect on the utility-type risk factor in the case is shown in the figure below:

The code of the perceived value formula for each key risk factor in the face of different scenarios is shown in the following figure:

The running result is shown as follows:

四、知识补充——Pearson系数（知识补充—Pearson系数）

Pearson 相关系数，也称为 Pearson 积差相关系数，是由英国统计学家卡尔·皮尔逊于1895年提出的。它是一种用于衡量两个连续变量之间线性关系强度和方向的统计量。Pearson 相关系数的取值范围在 -1 到 1 之间，具体含义如下：

Pearson correlation coefficients, also known as Pearson product-difference correlation coefficients, were developed by English statistician Carl Pearson in 1895. It is a statistic used to measure the strength and direction of a linear relationship between two continuous variables. Pearson correlation coefficients range from -1 to 1 and have the following meanings:

r = 1：表示两个变量之间存在完全正相关，即一个变量的增加伴随着另一个变量的等比例增加。

r = 1: Indicates that there is a completely positive correlation between two variables, that is, the increase in one variable is accompanied by an equal proportion increase in the other variable.

r = -1：表示两个变量之间存在完全负相关，即一个变量的增加伴随着另一个变量的等比例减少。

r = -1: Indicates that there is a completely negative correlation between two variables, i.e. an increase in one variable is accompanied by a proportional decrease in the other.

r = 0：表示两个变量之间不存在线性关系，即一个变量的变化与另一个变量的变化没有明显的线性关联。

r = 0: Indicates that there is no linear relationship between the two variables, that is, there is no obvious linear relationship between the change of one variable and the change of the other.

Pearson系数的计算公式为：

The formula for calculating the Pearson coefficient is:

Pearson系数在多个领域有着广泛的应用，包括心理学、社会学、生物学、经济学等。它不仅可以用来评估两个变量之间的线性关系程度，还可以帮助推断总体数据的相关性。然而，需要注意的是，Pearson系数对数据的正态性有一定要求，同时它仅适用于衡量线性关系，对于非线性关系则不适用。

The Pearson coefficient is used in a wide range of fields, including psychology, sociology, biology and economics. It can be used not only to evaluate the degree of linear relationship between two variables, but also to help infer the correlation of the overall data. However, it should be noted that the Pearson coefficient has certain requirements for the normality of the data, and it is only suitable for measuring linear relationships, not nonlinear relationships.

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