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引用次数: 0
摘要
冲突解决图模型(GMCR)是模拟和分析冲突的有用工具。在冲突中,决策者(DM)对可行状态的评估往往受到多种属性的影响。当处于不同的可行状态时,DM 可能会对每个属性赋予不同的重要性。因此,本文将多属性评估(MAE)应用于 GMCR,并提出了基于 MAE 的稳定性定义。此外,由于 DM 之间的关系和观点不同,对手对焦点 DM 的行动会做出不同的反应。为了分析对手的异质性行为,本文提出了一种基于社会网络分析(SNA)和意见相似性的异质性行为分析方法。然后,提出了基于 MAE 的混合稳定性定义,以进行考虑异质行为的稳定性分析。最后,本文将提出的方法应用于埃尔米拉污染冲突,并进行了敏感性分析,以证明所提方法的有效性。
Multi-Attribute evaluation-based graph model for conflict resolution considering heterogeneous behaviors
The graph model for conflict resolution (GMCR) is a useful tool for modeling and analyzing conflicts. In a conflict, the decision maker (DM)’s evaluation of feasible states is often influenced by multiple attributes. When in different feasible states, DMs may assign different importance to each attribute. Therefore, this paper applies multi-attribute evaluation (MAE) to GMCR and proposes the MAE-based stability definition. In addition, due to differences in relationships and opinions among DMs, opponents will behave differently in response to the action of the focal DM. To analyze the heterogeneous behavior of opponents, this paper proposes a heterogeneous behavior analysis method based on social network analysis (SNA) and opinion similarity. Then, the MAE-based mixed stability definitions are proposed to perform the stability analysis considering heterogeneous behaviors. Finally, this paper applies the proposed method to the Elmira contamination conflict and makes a sensitivity analysis to prove the validity of the proposed method.
期刊介绍:
Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions.
Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.