| 基于多层级移情网络的应急群决策建模 |
| Modeling of emergency group decision making based on multi-level empathetic networks |
| 摘要点击 34 全文点击 0 投稿时间：2025-02-28 修订日期：2026-01-03 |
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| 中文关键词 多层级移情决策模型; 拓扑结构; 社区结构; 不完全信息; 偏好解聚; 参数学习 |
| 英文关键词 multi-level empathetic decision-making model; topological structure; community structure; incomplete information; preference disaggregation; parameter learning |
| 基金项目 国家自然科学基金项目（面上项目，重点项目，重大项目） |
| 投稿方向 应急群决策 |
| 作者 | 单位 | 邮编 | | 沈思敏 | 南京信息工程大学经管学院管工学院 | 210044 | | 巩在武* | 南京信息工程大学经管学院管工学院 | 210044 |
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| 中文摘要 |
| 为探究应急决策专家间移情网络的拓扑结构和社区结构对决策效果的影响, 从层级角度对移情网络的内生和外生效应展开研究. 首先, 基于层次依赖Choquet积分提出多层级移情决策模型. 其次, 针对应急决策中信息贫乏问题, 基于偏好解聚思想构建参数学习模型, 并构建混合0-1整数规划模型识别不一致的决策信息. 再次, 为挖掘初始信息隐含的潜在信息, 构建相应模型以获得整体网络和各社区内方案间的必然和可能关系, 并基于此构建最具代表性的决策模型. 最后, 通过郑州暴雨灾害中方案选择的案例验证所构建模型的有效性. 结果表明, 这些模型不仅能够揭示方案间的偏好关系, 还能量化网络中的协同与冗余效应. |
| 英文摘要 |
| To explore the impact of the topological and community structures of the empathetic network among emergency decision-making experts on decision-making outcomes, this study examines the endogenous and exogenous effects of the empathetic network from a hierarchical perspective. Firstly, a multi-level empathetic decision-making model is proposed based on the Hierarchical Choquet integral. Secondly, addressing the issue of information scarcity in emergency decision-making, a parameter learning model is constructed based on preference disaggregation, and a mixed 0-1 integer programming model is developed to identify inconsistent decision-making information. Thirdly, to uncover the potential information in the initial data, models are developed to obtain the necessary and possible relationships between alternatives within the entire network and each community. Based on these relationships, the most representative decision-making model is constructed. Finally, the effectiveness of the models is verified through a case study of alternative selection in the Zhengzhou flood disaster. The results show that the models not only reveal the preference relationships between alternatives but also quantify the synergy and redundancy effects within the network. |
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