Consensus-trust Driven Bidirectional Feedback Mechanism for Improving Consensus in Social Network Large-group Decision Making

This paper proposes a consensus-trust driven framework of bidirectional interaction for social network large-group decision making. Firstly, the concepts of interaction consensus threshold and interaction trust threshold are defined, which are used to discriminate the interaction modes between subgroups into four categories. Corresponding hybrid feedback strategies are designed in which the consensus level and trust level of subgroups are regarded as reliable resources to facilitate the achievement of group consensus. Secondly, a minimum adjustment bidirectional feedback model considering cohesion is developed to help the interacting subgroups reach mutual consensus with minimum opinion modification. Finally, the proposed consensus framework is applied to a blockchain platform selection problem in supply chain to demonstrate the effectiveness and applicability of the model.

Automated summary

This paper proposes a consensus-trust driven framework for bidirectional interaction in social network large-group decision making. The framework includes defining interaction consensus threshold and interaction trust threshold, designing hybrid feedback strategies, and developing a minimum adjustment bidirectional feedback model considering cohesion. The effectiveness and applicability of the model are demonstrated through its application to a blockchain platform selection problem in supply chain.