The trust incentive mechanism by trust propagation to optimize consensus in social network group decision making

Abstract

Trust relationship in social network group decision making (SN-GDM) is one of the key factors to promote group consensus. This trust relationship reflects the importance of experts, and the consensus feedback opinion induced by the trust relationship is easier to be accepted by the group. But this trust relationship is often assumed to be static, and therefore the dynamic effect of incentive behavior among experts on trust relationship is not considered. To do that, the purpose of this paper is to develop a trust incentive mechanism to effectively promote group consensus. First, the new discount based Uninorm trust propagation operator with 2-tuple linguistic is presented, which transforms an incomplete trust relationship into a complete trust relationship. And then, the trust incentive mechanism with opinions similarity between experts is constructed, which reflects not only the incentive process among experts, but also the dynamic change of the social trust relationship. Further, the trust incentive mechanism driven consensus optimization model is established to generate experts’ recommendation advices, which not only effectively reduce the total adjustment cost of experts to promote consensus, but also makes the experts continue to motivate the discussion after revising their opinions, leading to dynamic changes in the social trust relationship in the next consensus round. Finally, an example of agricultural UAV brand selection in “Metaverse” intelligent shopping is provided to illustrate the proposed method, and the results show that with the increase of incentive factor, the total adjustment cost decreases, and the best UAV brand also changes alternately and tends to be stable. In addition, comparing with the method without trust incentive, the interaction cost of the proposed method is 13.46% lower, and then our proposed method has certain flexibility and advantages.