Maximum expert consensus model with uncertain adjustment costs for social network group decision making

In the realm of group decision making (GDM), the maximum expert consensus model (MECM) emerges as a potent tool for consensus optimization. The complexity of decision-making environment leads to the uncertainty of adjustment costs and the intricate social relationships between decision makers (DMs). Therefore, this paper aims to develop the MECM that integrates both social relationships and uncertain adjustment costs to support social network group decision making (SNGDM) problems. Specifically, we propose a MECM with quadratic cost that can more accurately reflect DMs' sensitivity for opinion adjustment. Additionally, we adopt an opinion modification mechanism based on the information obtained from the social network. The paper also develops the robust MECM (RMECM) to handle the uncertainty of the unit adjustment cost under three uncertain scenarios. Finally, the efficiency of the proposed models is demonstrated by applying them to the agricultural insurance premiums subsidy policymaking in China, further substantiated by sensitivity analysis and comparative analysis showcasing their robust performance.