Gregory James Ewing , Ibrahim Demir

Sensors and control technologies are being deployed extensively in both urban water networks and rural river systems, leading to unprecedented ability to sense and control our water environment. Because these sensor networks and control systems allow for higher resolution monitoring and decision making in both time and space, greater discretization of control will allow for an unprecedented precision of impacts, both positive and negative. Likewise, due to growth in system complexity, humans will continue to cede direct decision-making powers to decision-support technologies such as data algorithms. Systems will have ever-greater potential to effect human lives and yet humans will be insulated from direct decisions. Combined, these trends present a challenge for water resources management decision support tools to incorporate concepts ethical and normative expectations. Towards this end, we propose the Water Ethics Web Engine, (WE)2, an integrated and generalized web framework to incorporate voting-based ethical and normative preferences into water resources decision-support schemes. We then demonstrate the framework with a proof-of-concept use case where decision models are learned and deployed to respond to flooding scenarios. Results indicate the framework can capture group “wisdom” in learned models and use this to make decisions. We share our generalized framework and its cyber components openly with the research community.