一个新的普通共识模型,用于多个属性组决策与不完整的社交网络信任偏好关系
IEEE transactions on cybernetics
|July 24, 2025
概括
本研究引入了一种新的顺序共识框架,用于使用不完整的社交网络信任偏好关系 (TPR) 进行多属性组决策 (MAGDM). 它通过提高共识建设效率来提高决策质量.
科学领域:
- 决策科学 决策科学
- 运营研究 运营研究
- 人工智能的人工智能
背景情况:
- 多重属性小组决策 (MAGDM) 帮助小组从多个选项中选择最佳替代方案.
- 为了提高MAGDM的决策质量,在专家之间达成共识至关重要.
- 现有的共识模型面临着不完整的社交网络信任偏好关系 (TPR) 的挑战.
研究的目的:
- 为MAGDM提出一个新的顺序共识框架,包括不完整的社交网络TPRs.
- 加强共识形成过程,改善MAGDM的决策成果.
- 解决意见分歧,有效地达成协商一致的决策.
主要方法:
- 使用不完整的语言偏好关系来代表专家的社交网络TPRs.
- 开发了一种以一致性为导向的两阶段优化方法,以获得个人信任水平和专家权重.
- 设计了一个基于距离的顺序共识测量和基于意见动态的反调整机制.
主要成果:
- 拟议的框架有效地处理在MAGDM中不完整的社交网络TPR.
- 正规的共识过程提高了共识决策结果的有效性.
- 模拟实验和案例研究证明了框架在建立共识方面的可用性和效率.
结论:
- 该研究为MAGDM提供了关于管理不完整社交网络TPR的新视角.
- 新的顺序共识过程显著改善了在复杂的决策场景中达成共识.
- 这些发现为实际的社交网络MAGDM问题提供了有价值的见解.
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