在犹不决的模糊图形模型框架内,复合决策者之间的共识稳定性,适用于医生与患者之间的纠纷
Tianming Zhang1, Dayong Wang2, Yejun Xu3
1College of Business, Yancheng Teachers University, Yancheng, 224007, People's Republic of China.
Scientific reports
|July 9, 2025
概括
本研究介绍了使用犹模糊偏好关系 (HFPR) 解决医生与患者争端的复合决策者 (CDM). 该模型增强了群体决策中的共识和稳定性,以促进社会和.
科学领域:
- 决策科学 决策科学
- 社会科学 社会科学 社会科学
- 计算社会科学 计算社会科学
背景情况:
- 由于社交媒体,医生与病人之间的争端正在升级,将个人冲突转化为群体问题.
- 现有的冲突解决模式与群体动态和不断变化的偏好的复杂性作斗争.
- 需要一个框架来管理争端中的多个利益相关者之间的共识和稳定性.
研究的目的:
- 在冲突解决的图形模型 (GMCR) 中,提出一种新的冲突解决框架,使用组合决策者 (CDM).
- 开发方法来表示和测量CDM之间的共识犹不决的模糊偏好关系 (HFPRs).
- 将开发的模型应用于中国的医生和患者纠纷,以促进和解和社会和.
主要方法:
- 使用犹不决的模糊偏好关系 (HFPRs) 来建模群体内的个人偏好.
- 开发基于灰色关系分析的共识测量和改进的K-means集群算法,结合社会信任.
- 采用DeGroot意见演变模型来模拟子组意见互动,并得出新的共识稳定性定义.
主要成果:
- 一种方法可以将个人HFPR转化为CDM的群组模糊偏好.
- 在犹不决的模糊图形模型结构中,对于共识稳定的新定义.
- 证明了该模型对医生和患者之间的争端的应用,有助于冲突分析和和解.
结论:
- 拟议的GMCR框架与CDM和HFPR有效地模拟复杂的群体偏好,并促进共识建设.
- 开发的共识稳定性定义为多利益相关方场景中的冲突解决提供了关键的见解.
- 该模型为决策者和政府提供了一种有价值的工具,用于管理和解决社会纠纷,例如医生和患者之间的纠纷.
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