Watson, Abigail 1 ; Kasprzyk, Joseph 2
1 CU-Boulder
2 CU-Boulder
Increasingly, decision support systems seek to provide structured robust decision-making support for stakeholders and decision makers under the context of deep uncertainty. Deep uncertainty refers to situations in which stakeholders or decision makers do not know, or cannot fully agree upon, the full suite of risk factors within a planning problem (e.g., environmental change and corresponding socioeconomic effects). This presentation first briefly reviews robust optimization and scenario approaches that have been proposed to plan for systems under deep uncertainty. One recently introduced framework, Many Objective Robust Decision Making (MORDM), combines two techniques: evolutionary algorithm search and robust decision making methods. Evolutionary algorithm search is used to generate planning alternatives, and robust decision making methods are used to sample performance over a large range of plausible factors and, subsequently, choose a robust solution. Within MORDM, Pareto approximate tradeoff sets of solutions are used to balance objectives and examine alternatives.
However, MORDM does not currently incorporate the deeply uncertain scenario information into the search process itself. In this presentation, we highlight several avenues for doing so, that are focused on modifying the suite of uncertain data that is selected within the search process. Visualizations that compare tradeoff sets across different sets of assumptions can be used to guide decision makers? learning and, ultimately, their selection of several candidate solutions for further planning. For example, the baseline assumptions about probability distributions can be compared to optimization results under severe events to determine adaptive management strategies. A case study of water planning in the Lower Rio Grande Valley (LRGV) in Texas is used to demonstrate the approach. Our LRGV results compare baseline optimization with new solution sets that examine optimal management strategies under low inflow, high evaporation, and high demand scenarios that mimic possible scenarios of water management under climate change.
Ultimately, our approach seeks to provide another level of understanding presently unavailable to decision makers and stakeholders. By examining how planning strategies change under the proposed optimization framework, we show the impact of deep uncertainty assumptions of future hydrologic and socioeconomic decisions on the best strategies for mitigating undesirable change in the LRGV problem.