A graph model for conflict resolution with inconsistent preferences among large-scale participants

IF 4.8 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Fuzzy Optimization and Decision Making Pub Date : 2021-10-18 DOI:10.1007/s10700-021-09373-w
Tang, Ming, Liao, Huchang
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引用次数: 2

Abstract

As a flexible and powerful method to resolve strategy conflicts, the graph model for conflict resolution has drawn much attention. In the graph model for conflict resolution, decision-makers need to provide their preference information for all possible scenarios. Most existing studies assumed that decision-makers adopt quantitative representation formats. However, in some real-life situations, decision-makers may tend to use qualitative assessments due to their cognitive expression habits. In addition, stakeholders involved in a graph model can be a group that is composed of a large number of participants. How to manage these participants’ inconsistent preference assessments is also a debatable issue. To fit these gaps, in this study, we propose a graph model for conflict resolution with linguistic preferences, and this model allows participants to use inconsistent assessments. To do this, we first construct a linguistic preference structure, with the necessary concepts being defined. Then, four stability definitions for both a two-decision-maker scenario and an n-decision-maker scenario are introduced. To illustrate the usefulness of the proposed model, an illustrative example regarding the Huawei conflict is provided.

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大规模参与者偏好不一致时冲突解决的图模型
图模型作为一种灵活而强大的解决战略冲突的方法,受到了广泛的关注。在冲突解决的图模型中,决策者需要为所有可能的场景提供他们的偏好信息。现有研究大多假设决策者采用定量表征格式。然而,在现实生活中的一些情况下,决策者可能会由于他们的认知表达习惯而倾向于使用定性评估。此外,图模型中涉及的涉众可以是由大量参与者组成的组。如何管理这些参与者不一致的偏好评估也是一个有争议的问题。为了适应这些差距,在本研究中,我们提出了一个具有语言偏好的冲突解决图模型,该模型允许参与者使用不一致的评估。为此,我们首先构建一个语言偏好结构,并定义必要的概念。然后,介绍了两个决策者场景和n个决策者场景的四种稳定性定义。为了说明所提出模型的有效性,本文以华为冲突为例进行了说明。
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来源期刊
Fuzzy Optimization and Decision Making
Fuzzy Optimization and Decision Making 工程技术-计算机:人工智能
CiteScore
11.50
自引率
10.60%
发文量
27
审稿时长
6 months
期刊介绍: The key objective of Fuzzy Optimization and Decision Making is to promote research and the development of fuzzy technology and soft-computing methodologies to enhance our ability to address complicated optimization and decision making problems involving non-probabilitic uncertainty. The journal will cover all aspects of employing fuzzy technologies to see optimal solutions and assist in making the best possible decisions. It will provide a global forum for advancing the state-of-the-art theory and practice of fuzzy optimization and decision making in the presence of uncertainty. Any theoretical, empirical, and experimental work related to fuzzy modeling and associated mathematics, solution methods, and systems is welcome. The goal is to help foster the understanding, development, and practice of fuzzy technologies for solving economic, engineering, management, and societal problems. The journal will provide a forum for authors and readers in the fields of business, economics, engineering, mathematics, management science, operations research, and systems.
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