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

这笔赠款将支持模拟新的管理方法，以改进独立水资源使用者和利益攸关方之间的合作。这种合作并不自然地受到激励，因为有利于上游用户的行动往往会对下游用户产生负面影响。这些不对称的利益在三个关键领域创造了不合作行为的可能性：1)取水权，2)水质责任，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的法律协议，这对适应或政策改进造成了障碍。这项研究将探索新的管理方法，如基于市场的机制，以克服合作面临的这些挑战。这些系统的预期效率和公平收益将促进市政、工业和农业用水用户、处理厂和网络运营商以及自然环境的繁荣和福利。这项研究将考虑两个截然不同的用例：华盛顿特区阿纳卡斯蒂亚分水岭的城市河流恢复(用例1)，以及田纳西州鸭子河分水岭的经济发展和生态保护(用例2)。这项工作将在运筹学和水资源管理专家之间建立新的跨学科合作，促进科学和智力的进步。该工作将使用严格的数学技术，从基于非合作博弈论的一层和两层均衡问题的角度对确定性和随机性水利基础设施系统进行建模。开发的模型结合了工程、水政策、机器学习、风险分析、复原力规划和经济因素。这项工作的新奇之处在于，它以系统、统一和内生的方式考虑了所有实体及其相互作用的风险和收益，并允许系统运营者/监管机构在不确定和/或不断变化的条件下有效地平衡风险和成本。此外，预计这项工作将导致水平衡问题分解方法以及这类一般基础设施平衡问题的随机平衡模型的算法进步。此外，一个具有均衡约束的滚动水平随机数学计划将为水资源利益相关者开发战略学习算法，以随着时间的推移改善他们的决策。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.