汤华丽, 梁海明, 董玉成. 后悔规避行为下二分类群体决策方法[J]. 电子科技大学学报社科版, 2021, 23(2): 48-56. DOI: 10.14071/j.1008-8105(2021)-1001
引用本文: 汤华丽, 梁海明, 董玉成. 后悔规避行为下二分类群体决策方法[J]. 电子科技大学学报社科版, 2021, 23(2): 48-56. DOI: 10.14071/j.1008-8105(2021)-1001
TANG Hua-li, LIANG Hai-ming, DONG Yu-cheng. Binary Classification Group Decision Making Under the Regret Aversion Behaviors[J]. Journal of University of Electronic Science and Technology of China(SOCIAL SCIENCES EDITION), 2021, 23(2): 48-56. DOI: 10.14071/j.1008-8105(2021)-1001
Citation: TANG Hua-li, LIANG Hai-ming, DONG Yu-cheng. Binary Classification Group Decision Making Under the Regret Aversion Behaviors[J]. Journal of University of Electronic Science and Technology of China(SOCIAL SCIENCES EDITION), 2021, 23(2): 48-56. DOI: 10.14071/j.1008-8105(2021)-1001

后悔规避行为下二分类群体决策方法

Binary Classification Group Decision Making Under the Regret Aversion Behaviors

  • 摘要:
    目的/意义二分类群体决策问题广泛存在于社会民生和区域经济中的各个领域,例如:公务员面试、酒店推荐以及交通出行路线选择等。因此,需要提出合理的二分类群体决策方法,从而为政府和相关企业提供决策支持。
    设计/方法在考虑个体后悔规避行为的基础上,建立二分类选择过程,获得个体和群体关于备选方案的二分类向量;进一步建立二分类共识过程,协助个体调整偏好,从而获得具有较高共识水平的二分类。
    结论/发现从最小调整成本视角,建立了后悔规避行为下二分类群体决策方法,更加贴近现实决策情景,进一步丰富了群体决策方法的应用范围。

     

    Abstract: Purpose/Significance Binary classification group decision making problems are widespread in the fields of social life and regional economies, e.g., civil servant interview, hotel recommendation and traffic routes selection. Therefore, it is necessary to develop a reasonable binary classification group decision making to support the government and related enterprises. Design/Methodology A binary classification selection process is built based on the regret aversion behaviors of individuals to obtain the individual and collective binary classification vectors. A binary classification consensus process is then built to assist the individuals to adjust their preferences and improve their consensus level. Findings/Conclusions From the perspective of minimum adjustment cost, this paper develops a binary classification group decision making under the regret aversion behaviors. The proposed method, closer to real decision scenarios, further enriches the applications of group decision making.

     

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