An efficient consensus reaching framework for large-scale social network group decision making and its application in urban resettlement
Metadatos
Afficher la notice complèteEditorial
Elsevier
Materia
Large-scale decision-making Consensus reaching Dual social network Preference classification Minimum consensus cost
Date
2021-06-18Referencia bibliográfica
Xiangrui Chao... [et al.]. An efficient consensus reaching framework for large-scale social network group decision making and its application in urban resettlement, Information Sciences, Volume 575, 2021, Pages 499-527, ISSN 0020-0255, [https://doi.org/10.1016/j.ins.2021.06.047]
Patrocinador
National Natural Science Foundation of China (NSFC) 71874023 71771037 71971042 71910107002 71725001; Spanish State Research Agency PID2019-103880RB-I00/AEI/10.13039/501100011033Résumé
Urban resettlement projects involve a large number of stakeholders and impose tremendous
cost. Developing resettlement plans and reaching an agreement amongst stakeholders
about resettlement plans at a reasonable cost are some of the key issues in urban
resettlement. From this perspective, urban resettlement is a typical large-scale group
decision-making (GDM) problem, which is challenging because of the scale of participants
and the requirement of high consensus levels. Observing that residents who are affected by
a resettlement project often have tight social connections, this study proposes a framework
to improve the consensus reaching and uses the minimum consensus cost to reduce the
total cost for urban resettlement projects with more than 1000 participants. Firstly, we
construct a network topology that consists of two layers to deal with incomplete social
relationships amongst large-scale participants. An inner layer consists of participants
whose preference similarities and trust relations are known. Meanwhile, an outside layer
includes participants whose trust relations cannot be determined. Secondly, we develop
a classification method to classify participants into small subgroups based on their preference
similarities. We can then connect the participants whose trust relations are unknown
(the outside layer) with the ones in the inner layer using the classification results. To facilitate
effective consensus reaching in large-scale social network GDM, we develop a threestep
approach to reconcile conflicting preferences and accelerate the consensus process at
the minimum cost. A real-life urban resettlement example is used to validate the proposed
approach. Results show that the proposed approach can reduce the total consensus cost
compared with the other two practices used in the actual urban resettlement operations.