An interactive consensus reaching model with updated weights of clusters in large-scale group decision making

• Published in: Engineering applications of artificial intelligence 2022-01, Vol.107, p.104532, Article 104532
• Main Authors: Liao, Huchang, Wu, Zheng, Tang, Ming, Wan, Zhengjun
• Format: Article
• Language: English
• Subjects: Consensus reaching process; Interactive decision making; Large-scale group decision making; Minimum cost consensus; Modification degree
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2021.104532

Consensus reaching process generates a group decision approved by all experts despite their possible divergent preferences. To circumvent a calculated and nominal consensus without exchanges of views, an interactive consensus reaching strategy is necessary even though it may bring costs, especially for large-scale group decision making problems. To allow experts to make cost-effective preference modifications to reach consensus in large-scale group decision making, this study proposes a dynamic interactive consensus reaching model. Firstly, a minimum-cost-consensus model that focuses on clusters in a large-scale context is introduced, in which the unit preference adjustment cost can be determined objectively. Then, we apply the minimum-cost-consensus solution to develop a feedback mechanism to activate discussions between experts and support preference modifications. The modification degree is defined towards each expert cluster to measure the cost performance of a cluster pertaining to the modifications. On this basis, we update the weights of clusters so as to improve the cost performance. An illustrative example about the grading management of high-alert medication is presented. By comparison, the interactive consensus model only costs 10.1 percent more than an automatic consensus model but gets group consensus with the exchanges of expert views.