A social ties-based approach for group decision-making problems with incomplete additive preference relations
作者： Qian Liang , Xiuwu Liao , Jiapeng Liu
DOI: 10.1016/J.KNOSYS.2016.12.001
关键词: Computer science 、 Ranking 、 Social network 、 Preference 、 Interpersonal ties 、 Group decision-making 、 Artificial intelligence 、 Machine learning 、 Social influence 、 Social network analysis 、 Consistency (database systems) 、 Information transfer
摘要: With the rapid growth of Web 2.0 technology, a new paradigm has been developed that allows many users to participate in decision-making processes within online social networks. The information (i.e., ties and influence) members is stored networks provides perspective for investigating group (GDM) problems. In this paper, interactive GDM approach, based on networks, proposed address ranking problem with incomplete additive preference relations (IAPRs). This approach incorporates strength influence calculated by network analysis methods regarding process. After decision makers (DMs) provide IAPRs, searching algorithm identify optimal transfer path from DMs supporters who can corresponding information. Next, linear programming model constructed complete missing values IAPRs. main features include its ability account other maintain consistency. To help reach an agreement alternatives, consensus reaching process proposed. are used calculate acceptable adjustment coefficients feedback mechanism. Finally, illustrative example further discussion demonstrate validity approach.
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