在动态信任关系下,基于激励机制的最低调整共识模型
IEEE transactions on cybernetics
|January 23, 2024
概括
本研究引入了一种激励机制,通过鼓励专家调整偏好,通过动态的信任关系促进共识,并降低调整成本,以改善集团决策.
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:
- 传统的群体决策经常迫使专家妥协,阻碍达成共识.
- 现有的方法缺乏在动态环境中进行偏好调整的有效策略.
研究的目的:
- 开发一种新的激励机制,以在动态信任下实现群体共识.
- 降低偏好调整成本,促进专家合作.
主要方法:
- 定义了一个基于信任的最高和最低激励的规则.
- 开发了一个以激励行为为驱动的最低调整共识模型.
- 建立了一个基于信任交互的优化模型,用于最终的群体偏好.
主要成果:
- 拟议的机制有效地降低了偏好调整成本.
- 它通过激励偏好修订来促进群体共识.
- 通过激励驱动的修订来加强更新的信任关系.
结论:
- 新的激励机制增强了决策中的集团共识.
- 它提供了一个比强迫妥协更有效的方法.
- 通过高端医疗设备供应商选择案例来证明.
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