Decisionmaking Under Deep Uncertainty

Decision making under deep uncertainty (DMDU) is a decision science practice and analytical framework that evaluates potential solutions across multiple plausible future scenarios rather than attempting to predict a single future outcome. This approach is particularly valuable for strategic planning, public policy, and risk management when stakeholders, analysts, and decision-makers cannot reach consensus about future conditions or when traditional forecasting methods are inadequate due to fundamental uncertainties.

DMDU employs simulation models and scenario planning to explore potential futures through multiple "States of the World" (SOWs) and alternative scenarios, enabling comparison of how different policy options or decisions might perform across diverse possible outcomes. The methodology focuses on identifying robust and adaptive decisions that can perform well across a range of uncertain conditions, rather than optimizing for a single predicted future.

The term "deep uncertainty" distinguishes this approach from traditional decision theory and risk analysis, which typically assume that probabilities can be assigned to different outcomes. In contrast, DMDU is applied when uncertainties are so profound that multiple parties cannot agree on the appropriate probability distributions, system models, or even the range of possible outcomes. This framework has been increasingly applied to long-term challenges such as climate change adaptation, infrastructure planning, water resources management">infrastructure">öller, V., R. van Diemen, J.B.R. Matthews, C. Méndez, S. Semenov, J.S. Fuglestvedt, A. Reisinger ...