Managing fairness and consensus based on individual consciousness of preventing manipulation

Abstract

Decision makers (DMs) within a social network can communicate and compare with each other on recommendations and compensations received from moderator, which may arise the feelings of being manipulated and unfairness respectively. To prevent these two feelings from destroying the consensus, this paper proposes a maximum fairness consensus model based on individual consciousness of preventing manipulation (MFCM-PM) to provide reliable solutions in the social network group decision making. Specifically, we measure DMs’ fairness level based on compensations and trust relationships, which is inspired by social comparison and Gini coefficient. Furthermore, we propose a mechanism that triggers DMs' consciousness of preventing manipulation which will lead to the rejection regarding recommendations in consensus reaching process. Then, we construct the MFCM-PM model and investigate its several properties. Finally, for the proposed model, a numerical example is used to illustrate its applicability and simulation analyses are conducted to explore its effectiveness.