An approach to prevent weight manipulation by minimum adjustment and maximum entropy method in social network group decision making

Abstract

In social network group decision making (SN-GDM) problem, subgroup weights are mostly unknown, many approaches have been proposed to determine the subgroup weights. However, most of these methods ignore the weight manipulation behavior of subgroups. Some studies indicated that weight manipulation behavior hinders consensus efficiency. To deal with this issue, this paper proposes a theoretical framework to prevent weight manipulation in SN-GDM. Firstly, a community detection based method is used to cluster the large group. The power relations of subgroups are measured by the power index (PI), which depends on the subgroups size and cohesion. Then, a minimum adjustment feedback model with maximum entropy is proposed to prevent subgroups’ manipulation behavior. The minimum adjustment rule aims for ‘efficiency’ while the maximum entropy rule aims for ‘justice’ . The experimental results show that the proposed model can guarantee the rationality of weight distribution to reach consensus efficiently, which is achieved by maintaining a balance between ‘efficiency’ and ‘justice’ in the mechanism of assigning weights. Finally, the detailed numerical and simulation analyses are carried out to verify the validity of the proposed method.

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“…Typically, when objective weights are employed, the influence of separate decision-makers is diminished. The most recognized objective weighting approaches are the information entropy method (Sun et al, 2023;Vaid et al, 2022;Wu et al, 2023;, CRITIC (Criteria Importance Through Intercriteria Correlation) (Mishra et al, 2023;Zhong et al, 2023), and FANMA methods (Chatterjee & Chakraborty, 2023;.…”