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

这笔赠款将支持为改进独立水资源使用者和利益相关者之间的合作建立新的管理方法模型。这种合作不会自然地受到激励，因为对上游用户有利的行为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产生了不合作行为的可能性：1)取水权，2)水质责任，以及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。本研究将探索新的管理方法，如市场机制，以克服这些合作的挑战。这些系统的预期效率和公平收益将促进城市、工业和农业用水户的繁荣和福利；污水处理厂和管网运营商；还有自然环境。本研究将考虑两个截然不同的用例：华盛顿特区大都市阿纳科斯蒂亚流域的城市河流恢复（用例#1），以及田纳西州鸭河流域的经济发展和生态保护（用例#2）。这项工作将在业务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价值的进步。这项工作将使用严格的数学技术，基于非合作博弈论，从一级和两级均衡问题的角度对确定性和随机水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、弹性规划和经济因素。这项工作的新颖之处在于，它以系统的、统一的和内生的方式在所有实体及其相互作用中考虑风险和收益，并允许系统操作员/监管者在不确定和/或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的算法进步，以及这类基础设施平衡问题的随机平衡模型。此外，一个具有均衡约束的滚动视界随机数学程序将为水资源利益相关者开发战略学习算法，以改进他们的决策。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.