Research on Ordinal Regression Modeling of Incomplete Preference Relations Considering Indirect Preferences in Empathetic Network
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XIA Xuan, GONG Zaiwu
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School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044
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History+
Received
Revised
2024-05-30
2024-07-04
Just Accepted Date
2024-08-07
Abstract
In group decision-making, decision makers often demonstrate empathy towards others' opinions due to the incompleteness of information in preference relations. This empathy effect is conducive to inspiring experts to provide higher-quality judgments. Addressing incomplete fuzzy preference relations with unknown weights, this paper proposes the ordinal regression method for incomplete preference relations considering empathy relations by integrating indirect preference information with low cognitive requirements. Firstly, based on the transformed empathy-induced indirect preference information, we construct the ordinal regression completion model and the conflicting information adjustment model. Then, by combining assessment information including empathy centrality, influence strength, and consensus measure, as well as indirect node information, the utility of each node is determined as the weight of the decision-maker through the construction of the ordinal regression model. Finally, consensus convergence is achieved through the minimum cost adjustment model. The proposed method considers the impact of empathetic network and indirect preference information on missing values and node utilities, which not only resolves logical conflicts caused by rough indirect information but also reduces the cost of consensus and improves the consistency and reliability of estimation results. Case analysis and comparative discussions demonstrate the effectiveness of the proposed method.
XIA Xuan
, GONG Zaiwu. , {{custom_author.name_en}}.
Research on Ordinal Regression Modeling of Incomplete Preference Relations Considering Indirect Preferences in Empathetic Network. Journal of System Science and Mathematical Science Chinese Series, 2024 https://doi.org/10.12341/jssms240402