Managing manipulative and non-cooperative behaviors in large scale group decision making based on a WeChat-like interaction network

The main challenges in large scale group decision making (LSGDM) problem are how to tackle with the great number of participants and how to achieve a common solution accepted by most of group members. In LSGDM problems, some decision makers (DMs) will exhibit manipulative and non-cooperative behaviors owing to the different interests they might present. Dealing with such large group implies a need for mechanisms to detect DMs' manipulative and non-cooperative behaviors, which might affect the overall efficiency of the consensus reaching process. This paper introduces a novel framework based on WeChat-like interaction network to analyze manipulative and non-cooperative behaviors in the LSGDM problems. In the developed framework, we first detect and manage the manipulative behaviors based on the interaction network. Afterwards, for the consensus model based on opinion evolution, we develop an approach to identify and manage non-cooperative behaviors under the WeChat-like interaction network context. As a result, both the DMs' weights and trust network derived from it are dynamically updated in parallel. Detailed simulation experiments and comparison analysis under different input parameters are presented to demonstrate the efficiency of this novel approach for coping with manipulative and non-cooperative behaviors.

Automated summary

This paper introduces a novel framework based on WeChat-like interaction network to analyze manipulative and non-cooperative behaviors in large scale group decision making problems. By detecting and managing manipulative and non-cooperative behaviors, the efficiency of the consensus model is demonstrated through detailed simulation experiments and comparison analysis under different input parameters.