在地下水整治系统中达成决策的共识:一个强化学习决策模型,解决受意外事件影响的利益相关者的意见
Hao Chen1, Chenchen Tong2, Changxin Xu1
1Business School, Hohai University, Nanjing 211100, China; Jiangsu Research Center for Coastal Resources and Economy, China.
Water research
|December 14, 2025
概括
本研究引入了一个模拟优化框架,以帮助在地下水整治项目 (GRPs) 中进行适应性决策. 它增强了利益相关者的共识,在不确定性下增强了性和适应能力.
科学领域:
- 环境科学 环境科学
- 环境管理环境管理
- 决策科学 决策科学 决策科学
背景情况:
- 地下水整治项目 (GRPs) 面临着复杂的决策挑战,原因是长时间,环境不确定性和利益相关者的利益冲突.
- 意想不到的事件可能会破坏利益相关者的共识,改变优先事项并使项目管理复杂化.
- 适应性决策对于在长期GRP中应对这些复杂性至关重要.
研究的目的:
- 提出一个新的模拟优化框架,以支持GRP的自适应性决策.
- 在意外事件下建模利益相关者意见动态,优化共识建设策略.
- 在复杂的环境项目中提高决策过程的弹性和适应性.
主要方法:
- 一个模拟组件使用连接的弗里德金-约翰森模型来跟踪利益相关者的意见演变.
- 一个优化组件,集成真实选项理论和强化学习,用于适应性策略选择 (扩展,推迟,放弃).
- 在深度不确定性下分析共识动态和决策策略的框架应用.
主要成果:
- 发现一致的风险感知和意见融合可以加强共识的弹性.
- 推迟或放弃共识过程的能力在深度不确定性下显著提高了适应能力.
- 该框架有效地展示了在长期GRP中对利益相关方共识的系统管理.
结论:
- 拟议的模拟优化框架提供了一种系统方法,用于在GRP中管理利益相关者的共识.
- 调查结果强调了风险感知和意见融合对于弹性共识的重要性.
- 该框架为适应性环境决策提供了有价值的见解,涉及不确定性下的多个利益相关者.
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