摘要：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期刊论文《Multi-criteria decision support and uncertainty handling, propagation and visualisation for emerg
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今天小编为您带来“越览（144）——精读期刊论文
《Multi-criteria decision support
and uncertainty handling, propagation and
visualisation for emergency and
remediation management》的
3 不确定性处理、传播和可视化”。
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Today, the editor brings the
＂Yue Lan (144)：Intensive reading of the journal article
＇Multi-criteria decision support
and uncertainty handling, propagation and
visualisation for emergency and
remediation management’
3 Uncertainty handling, propagation, and visualization".
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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期刊论文《Multi-criteria decision support and uncertainty handling, propagation and visualisation for emergency and remediation management》的3 不确定性处理、传播和可视化。
This issue of tweets will introduce the 3 Uncertainty handling, propagation, and visualization of the intensive reading journal article "Multi-criteria decision support and uncertainty handling, propagation and visualisation for emergency and remediation management" 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）
本节介绍了在核应急与治理场景下，利用Monte Carlo方法在RODOS系统中进行不确定性建模、传播与可视化的全过程。
This section introduces the whole process of uncertainty modeling, propagation and visualization in the RODOS system using the Monte Carlo method in nuclear emergency and governance scenarios.
（一）案例背景（Case background）
该研究基于一个假设的核事故场景构建决策分析模型，设定在核电站严重事故发生后，释放出大量放射性物质并污染了农业区域。为了支持应急响应与治理决策，在一次德国组织的决策研讨中，通过问题结构化过程建立了一个以“总体效用”为顶层目标的属性树，涵盖“辐射效能”“资源占用”“社会接受度”和“影响”等核心决策指标，同时定义了多种对策选项以供决策比较。
The study built a decision analysis model based on a hypothetical nuclear accident scenario, which was set after a serious accident at a nuclear powerplant, releasing a large amount of radioactive materials and contaminating agricultural areas. In order to support emergency response and governance decisions, in a decision-making seminar organized by Germany, an attribute tree with "overall utility" as the top-level goal was established through the problem structuring process, covering core decision indicators such as "radiation efficiency", "resource occupation", "social acceptance" and "impact", and multiple countermeasure options were defined for decision comparison.
（二）不确定性建模与传播机制（Uncertainty modeling and popagation mchanisms）
由于应急系统中的预测涉及不确定性，研究针对源项（释放的放射性物质量）和风向两个关键变量进行建模，分别采用对数正态分布与正态分布描述其概率特性。借助Monte Carlo方法对变量进行多次抽样，并将样本输入系统进行并行仿真，从而在整个应急系统（包括污染预测、后果分析和效益评估）中实现不确定性的系统性传播。
Since the prediction in the emergency system involves uncertainty, the study modeled two key variables, the source term (the amount of radioactive material released) and the wind direction, using lognormal distribution and normal distribution to describe their probability characteristics respectively. The variables were sampled multiple times using the Monte Carlo method, and the samples were input into the system for parallel simulation, thus achieving systematic propagation of uncertainty in the entire emergency system (including pollution prediction, consequence analysis, and benefit evaluation).
（三）不确定性可视化方法与决策支持（Uncertainty visualization methods and decision support）
研究在Web-HIPRE平台中采用多情境分析方式处理决策不确定性，生成一组基于样本的决策表而非单一决策结果。通过选取最可能情境及其对应的5%和95%分位场景，利用堆叠柱状图展示不同对策的总体效用及其不确定区间，并显示各个评估指标的不确定性来源。这种可视化方式有助于决策者理解结果的稳健性和不确定性对判断的影响，尤其在对策效能相近时提供更清晰的识别依据。
The study uses a multi-scenario analysis approach to deal with decision uncertainty in the Web-HIPRE platform, generating a set of sample-based decision tables rather than a single decision result. By selecting the most likely scenario and its corresponding 5% and 95% quantile scenarios, a stacked bar chart is used to display the overall utility of different countermeasures and their uncertainty intervals, and to display the sources of uncertainty for each evaluation indicator. This visualization method helps decision makers understand the robustness of the results and the impact of uncertainty on judgment, especially providing a clearer basis for identification when the effectiveness of countermeasures is similar.
（四）敏感性分析（Sensitivity analysis）
除了输入数据不确定性，研究还考虑了多指标决策分析（MCDA）模型中参数设置（如指标权重）的不确定性影响。以“社会接受度”指标为例，敏感性分析展示了在不同情境（最可能、最优、最差）下的对策表现，从而帮助决策者评估当前最佳方案在面对权重变动时的结果稳定性与可行性，增强决策的透明度和适应性。
In addition to input data uncertainty, the study also considered the impact of uncertainty in parameter settings (such as indicator weights) in the multi-criteria decision analysis (MCDA) model. Taking the "social acceptance" indicator as an example, the sensitivity analysis shows the performance of countermeasures under different scenarios (most likely, optimal, and worst), thereby helping decision makers evaluate the stability and feasibility of the current best solution in the face of weight changes, and enhancing the transparency and adaptability of decision-making.
四、知识补充（Knowledge supplement）
在核应急管理与环境模拟等复杂系统中，输入变量往往具有显著的不确定性与偏态特征。例如，在核事故中，释放的放射性物质量（即源项）通常不能取负值，且其波动性较大，分布往往偏向正方向，这使得传统的正态分布难以准确描述其分布特性。因此，对数正态分布成为建模此类变量的重要工具。
In complex systems such as nuclear emergency management and environmental simulation, input variables often have significant uncertainty and skewness. For example, in a nuclear accident, the amount of radioactive material released (i.e., source term) usually cannot take negative values, and its volatility is large, and its distribution tends to be positive, which makes it difficult for the traditional normal distribution to accurately describe its distribution characteristics. Therefore, the lognormal distribution becomes an important tool for modeling such variables.
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翻译：谷歌翻译
参考资料：百度百科、Chat GPT
参考文献：Jutta Geldermann, Valentin Bertsch, Otto Rentz. Multi-criteria decision support and uncertainty handling, propagation and visualisation for emergency and remediation management [J]. Operations Research, 2006, 1(1): 755-760.
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