具有模糊框架的决策算法和高级财务管理政策的评估
Zijie Yu1, Jiongchen Kou2, Yuanxin Liu2
1School of Accounting, Zhongnan University of Economics and Law, Wuhan, 430071, Hubei, China. melody6662022@outlook.com.
这项研究提出了一个新的金融决策算法,使用圆形图像模糊的子平均值 (C-PFHM) 运算符. 在金融政策评估中,C-PFHM方法有效处理不确定性,在复杂的情景中显示出高可靠性.
科学领域:
- 决策科学
- 财政管理
- 模糊集合理论
背景情况:
- 金融政策评估往往涉及不确定性,犹和模糊性.
- 现有的方法可能难以有效地汇集各种专家意见.
- 图像模糊集和赫罗尼平均值为改善不确定性处理提供了潜力.
研究的目的:
- 为了引入一个新的决策算法,循环图片模糊的子平均值 (C-PFHM) 运算符.
- 制定一个多标准的决策框架,以评估不确定性下的财务管理政策.
- 结合图像模糊集和赫罗尼平均值的优势来聚合专家意见.
主要方法:
- 开发C-PFHM运算符,集成图像模糊集和赫罗尼平均值.
- 建立一个决策框架,包括数据收集,规范化,C-PFHM聚合,清晰化和排名.
- 该框架应用于评估政策选择与关键绩效指标的财务案例研究.
主要成果:
- 拟议的C-PFHM算法证明了在不确定性下对金融政策的有效评估.
- 通过参考方法 (C-PFHM-TOPSIS) 观察到高相关性 (斯皮尔曼的rho=0. 9815).
- 该算法在高可靠性决策环境中被证明是可靠的.
结论:
- C-PFHM运营商为复杂的财务决策提供了一个强大的工具.
- 开发的框架有助于管理不确定的和模糊的专家信息.
- 这种方法提供了一种可靠且经过验证的金融政策评估方法.
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