A two-stage multi-attribute group consensus model based on distributed linguistic assessment information from the perspective of fairness concern. (June 2023)

• Record Type: Journal Article
• Authors: Zhang, Shanli; Zhang, Shitao; Ma, Zhenzhen; Liu, Xiaodi
• Is Part Of: Adaptive behavior. Volume 31:Number 3(2023)
• Page Start: 213
• Page End: 238
• Publication Date: 2023-06
• Subjects: distributed linguistic assessment information -- fairness concern -- individual fairness perceived utility -- fairness satisfaction degree -- two-stage consensus model -- urban flood disaster; Animal behavior -- Periodicals; Animals -- Adaptation -- Periodicals; Adaptability (Psychology) -- Periodicals; Adaptation, Psychological -- Periodicals; Artificial intelligence -- Periodicals; 591.5
• DOI: 10.1177/10597123221126208
• Languages: English
• ISSNs: 1741-2633

Abstract

The consensus reaching process (CRP) is a key step in group decision-making (GDM), in which reaching a satisfactory consensus often requires certain costs, such as time, money, and effort. Moreover, benefits from the CRP are often compared among decision-makers, which raises fairness issues. Given these, this paper comprehensively considers these two factors in linguistic GDM modeling. Aiming at the problem of multi-attribute group decision-making (MAGDM) under distributed linguistic assessment (DLA) information, a two-stage group consensus model that considers psychological behaviors with fairness concern is proposed. Subsequently, the feasibility and effectiveness of the proposed model are illustrated by a case of selecting emergency material warehouses related to urban flood disasters. The main innovations and contributions of this paper are as follows. (a) The psychology of individual fairness concern is integrated into the MAGDM process with DLA information. (b) The DLA-based two-stage group consensus model, including the first-stage consensus model with maximum fairness satisfaction degree and the second-stage consensus model with minimum cost, is developed. Compared with the existing consensus models for distributed linguistic MAGDM, the proposed consensus model can not only improve the consensus level on the opinions with minimum cost but also promote the fairness satisfaction degree of decision-makers.