Adaptive personalized large-scale group decision-making model based on psychobehavior in comprehensive trust network

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications Pub Date : 2025-10-10 DOI:10.1016/j.eswa.2025.129863

Wei Yang, Yizhuo Wang

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引用次数: 0

Abstract

Consensus decision-making methods for large-scale group decision-making problems in trust networks have become an important research direction of decision science. An adaptive personalized feedback mechanism based on self-confidence-certainty and psychological discrepancy in the consensus reaching process is proposed for the large-scale group decision-making problem under social trust network. Firstly, a comprehensive trust network is constructed based on trust familiarity and trust similarity. Secondly, the subgroup weights are calculated based on internal cohesion and external cohesion, and the social network DeGroot model is extended by considering the self-confidence-certainty and personalization factors. Thirdly, based on the self-confidence-certainty and psychological discrepancy measurement, adaptive personalized feedback mechanism is used to classify experts into four classes considering experts’ willingness and psychological behaviors. Finally, the method is validated by the problem of ‘east data, west computing’ project selection.

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基于心理行为的综合信任网络自适应个性化大规模群体决策模型

信任网络中大规模群体决策问题的共识决策方法已成为决策科学的重要研究方向。针对社会信任网络下的大规模群体决策问题，提出了一种基于共识达成过程中的自信确定性和心理差异的自适应个性化反馈机制。首先，基于信任熟悉度和信任相似度构建了一个全面的信任网络。其次，基于内部凝聚力和外部凝聚力计算子群权重，并考虑自信确定性和个性化因素对社会网络DeGroot模型进行扩展；第三，在自信确定性和心理差异测量的基础上，采用自适应个性化反馈机制，考虑专家的意愿和心理行为，将专家分为四类。最后，通过“东数据，西计算”项目选择问题对该方法进行了验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Expert Systems with Applications 工程技术-工程：电子与电气

CiteScore

13.80

自引率

10.60%

发文量

2045

审稿时长

8.7 months

期刊介绍： Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.