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

University Of Maryland, College Park, College Park MD

Investigators

Abstract

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.