Robust Consensus Modeling: Concerning Consensus Fairness and Efficiency with Uncertain Costs

Xuyuan Zhang, Hailin Liang and Shaojian Qu ()
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Xuyuan Zhang: School of Economics and Management, Shanghai Ocean University, Shanghai 201306, China
Hailin Liang: School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Shaojian Qu: School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

Mathematics, 2024, vol. 12, issue 8, 1-31

Abstract: Extensive uncertainty can affect the efficiency and fairness of consensus in the consensus reaching process (CRP), but few scholars have studied consensus modeling that focuses on fairness and efficiency in uncertain environments. Additionally, the weight of the decision maker (DM) in the CRP is influenced by multiple factors. Therefore, this paper proposes robust consensus models (EFCMs) focusing on fairness and efficiency under uncertain costs to address these issues. Firstly, this paper constructs multiple uncertainty sets to describe the uncertainty of the unit adjustment cost. Secondly, the fair utility level and opinion adjustment distance are used to measure the fairness and efficiency of reaching consensus, respectively. Furthermore, this paper uses a data-driven method based on the KDE method combined with trust propagation in social networks to determine the DMs’ weights jointly. Finally, this paper also applies the proposed models to the carbon emission reduction negotiation process between the government and enterprises. The experimental results and sensitivity analysis show that the consensus cost budget and the DMs’ jealous preference behavior particularly affects the efficiency of reaching consensus, which provides a theoretical basis for solving practical decision making problems.

Keywords: consensus model; fairness; efficiency; uncertainty cost; data-driven (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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