Browsing by Author "Xu, Weijun"

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    Evolution of Credit Scores of Enterprises in a Social Network: A Perspective Based on Opinion Dynamics
    (IEEE, 2024-02-09) Liang, Haiming; Xu, Weijun; Chiclana, Francisco; Yu, Shui; Dong, Yucheng; Herrera-Viedma, Enrique
    The use of social network to model the evolution of credit scores of networked enterprises is still a challenging task. This article develops an opinion dynamics model of the evolution of credit scores of enterprises in a social network. Firstly, based on the number of potential cooperated enterprises and the initial credit scores, the leader and follower enterprises are identified. Then, taking into consideration the cooperated benefit and discrimination cost, the cooperated utility between any two enterprises is calculated, which is used to compute the weights that one enterprise assigns to other enterprises. An opinion dynamics model on the evolution of credit scores of enterprises, inspired on the classical Friedkin–Johnsen’s social network model, is developed. Some desirable properties of the proposed opinion dynamics model are theoretically stated and proved. Finally, a numerical example is provided to illustrate the feasibility of the proposed opinion dynamics model, while a simulation analysis to investigate the joint influences of the connection probabilities and the network structure on the evolution of credit scores of enterprises is reported.
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    Impact of Decision Rules and Non-Cooperative Behaviors on Minimum Consensus Cost in Group Decision Making
    (Springer, 2020) Xu, Weijun; Chen, Xin; Dong, Yucheng; Chiclana, Francisco
    In group decision making (GDM), it is sensible to achive minimum consensus cost (MCC) because the consensus reaching process (CRP) resources are often limited. In this endeavour, though, there are still two issues that require paying attention to: (1) the impact of decision rules, including decision weights and aggregation functions, on MCC; and (2) the impact of non-cooperative behaviors on MCC. Hence, this paper analytically reveals the decision rules to minimize MCC or maximize MCC. Furthermore, detailed simulation experiments show the joint impact of non-cooperative behavior and decisions rules on MCC, as well as revealing the effect of the consensus within the established MCC target.