Social network group decision making: Characterization, taxonomy, challenges and future directions from an AI and LLMs perspective

IF 14.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Mingshuo Cao , Tiantian Gai , Jian Wu , Francisco Chiclana , Zhen Zhang , Yucheng Dong , Enrique Herrera-Viedma , Francisco Herrera
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引用次数: 0

Abstract

In the past decade, social network group decision making (SNGDM) has experienced significant advancements. This breakthrough is largely attributed to the rise of social networks, which provides crucial data support for SNGDM. As a result, it has emerged as a rapidly developing research field within decision sciences, attracting extensive attention and research over the past ten years. SNGDM events involve complex decision making processes with multiple interconnected stakeholders, where the evaluation of alternatives is influenced by network relationships. Since this research has evolved from group decision making (GDM) scenarios, there is currently no clear definition for SNGDM problems. This article aims to address this gap by first providing a clear definition of the SNGDM framework. It describes basic procedures, advantages, and challenges, serving as a foundational portrait of the SNGDM framework. Furthermore, this article offers a macro description of the literature on SNGDM over the past decade based on bibliometric analysis. Solving SNGDM problems effectively is challenging and requires careful consideration of the impact of social networks among decision-makers and the facilitation of consensus between different participants. Therefore, we propose a classification and overview of key elements for SNGDM models based on the existing literature: trust models, internal structure, and consensus mechanism for SNGDM. This article identifies the research challenges in SNGDM and points out the future research directions from two dimensions: first, the key SNGDM methodologies and second, the opportunities from artificial intelligence technology, in particular, combining large language models and multimodal fusion technologies. This look will be analyzed from a double perspective, both from the decision problem and from the technology views.
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来源期刊
Information Fusion
Information Fusion 工程技术-计算机:理论方法
CiteScore
33.20
自引率
4.30%
发文量
161
审稿时长
7.9 months
期刊介绍: Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.
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