基于犹和不确定的语言学,使用风险和遗憾厌恶的执行决策:方法,实施和分析
Taniya Seth1, Pranab K Muhuri1
1Department of Computer Science, South Asian University, Maidan Garhi, Rajpur Road, New Delhi-110068, India.
MethodsX
|April 25, 2024
概括
流行病等全球危机对高风险公司构成了挑战. 本研究介绍了THREAD模型,使用遗憾理论和模糊逻辑,以帮助在不确定性和犹的情况下进行最佳决策,以提高企业的弹性.
科学领域:
- 决策科学 决策科学
- 运营管理 运营管理
- 模糊逻辑应用 模糊逻辑应用
背景情况:
- 全球事件,以COVID-19大流行为例,显著扰乱了高风险的公司运营.
- 在危机中保持市场相关性和实现内部目标需要强大的决策框架.
- 现有的模型可能无法充分解决与前所未有的破坏相关的固有犹和风险.
研究的目的:
- 为面临运营中断的高风险公司引入一种新的语言决策模型.
- 将人类的认知,风险和遗憾纳入结构化的决策过程中.
- 用先进的模糊集技术来解决决策中的固有犹.
主要方法:
- 发展了THREAD (基于遗憾的犹模糊的语言决策理论) 模型.
- 使用遗憾理论原则来告知最佳选择.
- 使用间隔类型-2模糊集 (IT2 FS) 和犹不决的模糊语言术语集来处理不确定性和模糊性.
主要成果:
- 在不确定性和犹的条件下,THREAD模型为决策提供了一个计算框架.
- 展示了遗憾理论与模糊的语言技术的整合,以实现实际应用.
- 提供一种计算最佳决策的方法,考虑到潜在的风险和遗憾.
结论:
- 在全球危机期间,THREAD模型为高利益关的公司提供了一种有价值的工具,以导航复杂的决策.
- 在破坏性环境中有效的决策需要考虑诸如遗憾和犹之类的认知因素.
- 模糊的语言方法,特别是IT2FS和犹的模糊集,对于建模这些复杂的决策问题是有效的.
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