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Group Decision Making with Heterogeneous Preference Structures: An Automatic Mechanism to Support Consensus Reaching

机译:基团决策与异质偏好结构:支持达成共识的自动机制

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摘要

In real-world decision problems, decision makers usually express their opinions with different preference structures. In order to deal with the heterogeneous preference information in group decision making, this paper presents an optimization-based consensus model for group decision making with heterogeneous preference structures (utility values, preference orderings, multiplicative preference relations and additive preference relations). This proposal seeks to minimize the information loss between decision makers' heterogeneous preference information and individual preference vectors and also seeks the collective solution with a consensus. Meanwhile, in order to justify the consensus model, we discuss its internal aggregation operator between the obtained individual and group preference vectors, demonstrate that the proposed model satisfies the Pareto principle of social choice theory, and prove the uniqueness of the solution to the optimization model. Furthermore, based on the proposed optimization-based consensus model, we present an automatic mechanism to support consensus reaching in the group decision making with heterogeneous preference structures. In the consensus reaching process, the obtained individual and group preference vectors are considered as a decision aid which decision makers can use as a reference to adjust their preference opinions. Finally, detailed simulation experiments and comparison analysis are conducted to demonstrate the feasibility and effectiveness of our proposed model.
机译:在现实世界决策问题中,决策者通常用不同的偏好结构表达他们的意见。为了处理组决策中的异构偏好信息,本文提出了一种基于优化的共识模型,用于基于异构偏好结构(公用事业价值,偏好排序,乘法偏好关系和添加剂偏好关系)。该提案旨在最大限度地减少决策者异构偏好信息和个人偏好向量之间的信息损失,并在共识中寻求集体解决方案。同时,为了证明共识模型,我们在获得的个人和群体偏好向量之间讨论其内部聚集运算符,表明该拟议模型满足了社会选择理论的帕累托原则,并证明了解决优化模型的唯一性。此外,基于所提出的基于优化的共识模型,我们提出了一种自动机制,以支持与异构偏好结构的组决策达成的共识。在共识到期过程中,所获得的个体和群体偏好向量被认为是决策者可以用作调整偏好意见的参考的决策者。最后,进行了详细的仿真实验和比较分析,以证明我们提出的模型的可行性和有效性。

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