Multi-Agent Sustainable WaterDecision Theory (MUST): Nexus of Water, Road, and Hierarchic SocialContractual Systems
Providing reliable clean water is essential for the health and prosperity of communities. Our society, however, frequently faces the disastrous consequences of decisions made without considering their socio-economic context or the interdependency of critical infrastructure systems and services. Examples include incidents of poisonous algae in the water supply in Toledo, OH in summer 2014 and more recently, the lead contaminated water in Flint, MI which led to a declaration of a State of Emergency by the federal government. A common observation is that decisions that are primarily based on short-term cost considerations, such as those made by many municipal and city offices when facing resource constraints, can exacerbate water problems rather than improve them. This transdisciplinary research develops a water infrastructure investment decision support (WIIDS) model, calibrated with a rich repository of data. WIIDS places sustainable water services in a broad socioeconomic context that considers the inherent interdependencies of systems and interactions with the contractual, physical, and service infrastructures essential to communities.
This research formulates an advanced decision model that builds on a large repository of data to support water-related decisions at different levels. Particularly, the model describes the interdependencies of water systems, road systems (that provide mobility of people and goods while applying loads on the water pipes via mechanical and environmental processes), and the contractual systems (that affect community service decisions that influence the durability of water pipes). The model consists of two distinct parts. One is an engineering sub-model that uses historical data and forecast conditions to predict the probability of failure and the associated costs for the selected sections of water supply infrastructure. Another is a socioeconomic sub-model that utilizes input from the engineering sub-model along with current and forecast business impacts, traffic impact, community demographics, and critical infrastructures (e.g., hospitals, schools) to provide holistic repair/replace/reroute recommendations. The model allows comparisons among alternative decisions (e.g. reactive vs predictive maintenance) considering social costs, indirect economic costs, and public health implications besides the direct solution costs.