What Deep Uncertainty Is
I am specifically trained in decision-making under "deep uncertainty". Traditional methods for decision-making under uncertainty assume that uncertain outcomes can always be modeled with a probability distribution—that even if we don't know the exact outcome, we can guess the likelihood of each possible outcome in the same way that we know a fair 6-sided die has a 1/6 chance of landing on each number.
That's often not the case. Sometimes all we can do is propose an upper and lower bound, shrug our shoulders, and say "it's probably somewhere in there." We call that deep uncertainty.
Why It Matters
Deep uncertainty is the normal condition for long-horizon decisions about the climate and the economy. The temptation is either to pick a single "best estimate" scenario and plan against it—which produces plans that fail as soon as the world does something else—or to declare the problem intractable and fall back on intuition. Neither is necessary. Structured approaches exist for deriving actionable insight despite profoundly limited knowledge of the future, and they generally work by shifting the question from "what will happen?" to "under what conditions does this strategy fail, and how much do I care?"
That reframing is the core of how I approach this work: map the full space of decisions and uncertain futures, isolate the thresholds between success and failure, and identify strategies that are robust across the range of futures you can't rule out.
Further Reading
- Decision Making Under Deep Uncertainty — a review of structured approaches for deriving actionable insight despite profoundly limited knowledge of the future. Annual Review of Resource Economics.