Game Theoretic Modeling for Improved Management of Water and Wastewater Resources Using Equilibrium Programming and Feedback Mechanisms

利用平衡规划和反馈机制改进水和废水资源管理的博弈论模型

• 批准号: 2113891
• 负责人: Steven Gabriel
• 金额: $ 55.34万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113891&HistoricalAwards=false
• 关键词: Game; Theoretic; Modeling; Improved; Management

This grant will support the modeling of novel management approaches for improved cooperation among independent water resources users and stakeholders. Such cooperation is not naturally incentivized because the actions beneficial to upstream users can often negatively impact downstream users. These asymmetrical benefits create the potential for non-cooperative behavior in three key areas: 1) water withdrawal rights, 2) water quality responsibilities, and 3) risks associated with flooding. Historically, cooperative agreements among independent entities have required static legal agreements that created barriers to adaptation or policy improvement. This research will explore novel management approaches, such as market-based mechanisms, to overcome these challenges to cooperation. The anticipated efficiency and equity gains in these systems will advance prosperity and welfare for municipal, industrial, and agricultural water users; treatment plant and network operators; and the natural environment. Two contrasting use cases will be considered in this research: urban river restoration in the Anacostia Watershed (Use Case #1) in the metropolitan Washington, DC, area and economic development and ecological preservation in the Duck River Watershed (Use Case #2) in Tennessee. The work will establish new interdisciplinary collaborations between experts in operations research and water resources management, which will promote the progress of science and intellectual merit.The work will use rigorous mathematical techniques to model deterministic and stochastic water infrastructure systems from a one-level and two-level equilibrium problem perspective based on non-cooperative game theory. The developed models combine engineering, water policy, machine learning, risk analysis, resilience planning and economic elements. The novelty of this work is that it accounts for risk and benefits in a systematic, unified, and endogenous manner across all entities and their interactions and allows the system operator/regulator to effectively balance risk and cost under uncertain and/or changing conditions. Furthermore, it is anticipated that the work will lead to algorithmic advances in decomposition methods for water equilibrium problems as well as stochastic equilibrium models for this general class of infrastructure equilibrium problems. Also, a rolling-horizon, stochastic mathematical program with equilibrium constraints will develop strategic learning algorithms for water stakeholders to improve their decision-making over time.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该赠款将支持新颖管理方法的建模，以改善独立水资源用户和利益相关者之间的合作。这种合作并没有自然激励，因为对上游用户有益的动作通常会对下游用户产生负面影响。这些不对称的益处在三个关键领域中产生了非合作行为的潜力：1）水戒断权，2）水质责任和3）与洪水相关的风险。从历史上看，独立实体之间的合作协议需要静态法律协议，以造成适应或改善政策的障碍。这项研究将探索新颖的管理方法，例如基于市场的机制，以克服这些挑战的合作。这些系统的预期效率和股权提高将推动市政，工业和农业用水使用者的繁荣和福利；处理厂和网络运营商；和自然环境。这项研究将考虑两种对比的用例：田纳西州大都会华盛顿大都会华盛顿大都会华盛顿大都会，地区和经济发展以及田纳西州的经济发展以及生态保存的城市河流恢复（用例1）。这项工作将在运营研究和水资源管理专家之间建立新的跨学科合作，这将促进科学和知识分子的进步。这项工作将使用严格的数学技术来模拟从基于非配合游戏理论的一级和两级等价问题的确定性和随机水基础设施系统。开发的模型结合了工程，水政策，机器学习，风险分析，弹性计划和经济因素。这项工作的新颖性在于，它以系统的，统一和内生的方式来解释所有实体及其相互作用的风险和收益，并允许系统操作员/调节器有效平衡不确定和/或变化条件下的风险和成本。此外，预计这项工作将导致水平问题分解方法的算法进步，以及该一般基础设施平衡问题的随机平衡模型。此外，具有均衡约束的滚动效果，随机数学计划将为水利益相关者开发战略性学习算法，以随着时间的推移改善其决策。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力和更广泛影响的评估来通过评估来支持的，这是值得的。