An Open Source Framework for Many-Objective Robust Decision Making

Published in: Environmental Modelling and Software, v. 74, Dec. 2015, p. 114-129

by David Hadka, Jonathan Herman, Patrick M. Reed, Klaus Keller

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This study introduces a new open source software framework to support bottom-up environmental systems planning under deep uncertainty with a focus on many-objective robust decision making (MORDM), called OpenMORDM. OpenMORDM contains two complementary components: (1) a software application programming interface (API) for connecting planning models to computational exploration tools for many-objective optimization and sensitivity-based discovery of critical deeply uncertain factors; and (2) a web-based visualization toolkit for exploring high-dimensional datasets to better understand system trade-offs, vulnerabilities, and dependencies. We demonstrate the OpenMORDM framework on a challenging environmental management test case termed the "lake problem". The lake problem has been used extensively in the prior environmental decision science literature and, in this study, captures the challenges posed by conflicting economic and environmental objectives, a water quality "tipping point" beyond which the lake may become irreversibly polluted, and multiple deeply uncertain factors that may undermine the robustness of pollution management policies. The OpenMORDM software framework enables decision makers to identify policy-relevant scenarios, quantify the trade-offs between alternative strategies in different scenarios, flexibly explore alternative definitions of robustness, and identify key system factors that should be monitored as triggers for future actions or additional planning. The web-based OpenMORDM visualization toolkit allows decision makers to easily share and visualize their datasets, with the option for analysts to extend the framework with customized scripts in the R programming language. OpenMORDM provides a platform for constructive decision support, allowing analysts and decision makers to interactively discover promising alternatives and potential vulnerabilities while balancing conflicting objectives.

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