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（140）——精读期刊论文
《Multi-criteria decision support
and uncertainty handling, propagation and
visualisation for emergency and
remediation management》的
摘要”。
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Dear, this is the LearingYard Academy!
Today, the editor brings the
＂Yue Lan (140)：Intensive reading of the journal article
＇Multi-criteria decision support
and uncertainty handling, propagation and
visualisation for emergency and
remediation management’
abstract".
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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》的摘要。
This issue of tweets will introduce the abstract 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）
（一）研究背景及目标（Research background and objectives）
本篇期刊论文的摘要，首先介绍了论文的研究背景：RODOS（Real-time Online Decision Support System）是欧洲在应对核或放射性紧急事件中提供支持的实时在线决策支持系统。为了提升对长期应对措施的评估透明度，系统集成了多准则决策支持工具Web-HIPRE，用以反映决策团队的偏好。然而，现实中的决策过程不可避免地受到多种不确定性的影响，因此在决策系统中引入对不确定性的建模与传播具有重要意义。
The abstract of this journal article first introduces the research background of the paper: RODOS (Real-time Online Decision Support System) is a real-time online decision support system that provides support in Europe in responding to nuclear or radiological emergencies. In order to improve the transparency of the evaluation of long-term response measures, the system integrates the multi-criteria decision support tool Web-HIPRE to reflect the preferences of the decision-making team. However, the decision-making process in reality is inevitably affected by multiple uncertainties, so it is of great significance to introduce the modeling and propagation of uncertainty in the decision-making system.
然后，提出了本文的研究目标：本研究旨在建立一种能够一致性建模并传播不确定性的分析方法，以增强RODOS系统，特别是在与Web-HIPRE集成使用时，对不确定性结果的可视化和沟通能力。核心目标在于提高决策结果的可信度和透明度，从而更好地支持应对核或放射性事件中的长期措施选择。
Then, the research objectives of this paper are proposed: This study aims to establish an analytical method that can consistently model and propagate uncertainty to enhance the visualization and communication capabilities of the RODOS system, especially when integrated with Web-HIPRE. The core goal is to improve the credibility and transparency of decision-making results, thereby better supporting the long-term selection of measures in response to nuclear or radiological incidents.
（二）研究方法与研究内容（Research methods and content）
本文采用Monte Carlo方法对决策过程中的不确定性进行建模与传播分析。该方法通过在RODOS系统与Web-HIPRE集成的框架下模拟多种可能的情形，从而捕捉输入参数不确定性对最终决策结果的影响，并以直观的方式呈现不确定性传播过程，增强系统对复杂决策环境的适应性。
This paper uses the Monte Carlo method to model and propagate uncertainty in the decision-making process. This method simulates multiple possible scenarios under the framework of the integration of the RODOS system and Web-HIPRE to capture the impact of input parameter uncertainty on the final decision result, and presents the uncertainty propagation process in an intuitive way, enhancing the system's adaptability to complex decision-making environments.
研究表明，采用Monte Carlo方法能够在RODOS系统中有效传播和可视化不确定性，使得与Web-HIPRE结合的多准则决策分析在面对不确定条件时更具透明性与解释力。该方法有助于提升决策分析结果的可信度，为应对核或放射性紧急事件的长期管理措施提供更可靠的决策依据。
The study shows that the Monte Carlo method can effectively propagate and visualize uncertainty in the RODOS system, making the multi-criteria decision analysis combined with Web-HIPRE more transparent and explanatory when facing uncertain conditions. This method helps to improve the credibility of decision analysis results and provide a more reliable decision basis for long-term management measures in response to nuclear or radiological emergencies.
四、知识补充（Knowledge supplement）
Monte Carlo方法，也称蒙特卡洛模拟，是一种基于随机抽样和统计试验的数值计算方法。其核心思想是：当一个系统或过程具有高度复杂性或存在不确定性时，通过在输入参数上引入随机变量的概率分布，然后进行大量模拟试验，以逼近系统输出的统计特性。该方法并不依赖问题的解析解，而是通过大量“试验”逼近真实结果，特别适用于传统解析方法难以求解的多维度或随机问题。
Monte Carlo method, also known as Monte Carlo simulation, is a Numerical calculation method based on random sampling and statistical experiments. Its core idea is: when a system or process is highly complex or uncertain, the probability distribution of random variables is introduced on the input parameters, and then a large number of simulation tests are carried out to approximate the statistical characteristics of the system output. This method does not rely on analytical solutions to the Problem, but approximates the real results through a large number of "experiments", which is particularly suitable for multi-dimensional or random problems that are difficult to solve with traditional analytical methods.
1. 问题建模：将研究对象抽象成具有随机性的数学模型，明确输入变量及其概率分布（如正态分布、均匀分布等）。
1. Problem modeling: Abstract the research object into a mathematical model with randomness, and clarify the input variables and their probability distribution (such as normal distribution, uniform distribution, etc.).
2. 随机抽样：根据每个输入变量的概率分布，通过伪随机数生成器进行大量样本抽取。
2. Random sampling: A large number of samples are drawn through a pseudo-random number generator based on the probability distribution of each input variable.
3. 数值计算：对每组随机抽样数据进行模型计算，记录输出结果。
3. Numerical calculation: Perform model calculation on each set of randomly sampled data and record the output results.
4. 统计分析：对模拟输出的结果进行统计汇总（如均值、方差、置信区间等），以估计总体行为或风险分布。
4. Statistical analysis: Statistical summary of simulation outputs (e.g. mean, variance, confidence interval, etc.) to estimate overall behavior or risk distribution.
5. 可视化与解释：使用直方图、箱线图或置信区间图等方式，直观展示输出的不确定性特征和风险范围。
5. Visualization and interpretation: Use histograms, box plots, or confidence interval plots to visually display the uncertainty characteristics and risk range of the output.
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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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