Decision Making in Deep Uncertainty

Climate and societal risk under climate change is inherently uncertain, still under active research, and hampered by real gaps in prediction data. Institutions have often answered with single 'scores' — Decision Making under Deep Uncertainty (DMDU) offers a different approach: test many futures, see which policies survive.

• · A single climate risk score is either dishonest (false precision) or, once honestly hedged, too broad to be useful for a real decision
• · DMDU / Exploratory Modeling and Analysis (EMA) tests a policy against thousands of plausible futures instead of one forecast, and reports where it holds and where it breaks
• · Two real Swiss studies (urban heat stress, ski resort tipping points) show this in practice — and surface findings a single-scenario forecast would likely miss
• · DMDU has a real complexity/democratization critique we take seriously: better modelling can make decisions less accessible, not more, unless representation and UX are solved too