Walker et al. (2010) developed a taxonomy of “levels of uncertainty”, ranging from Level 1 to Level 4,
which is useful in problem-solving:
- Level 1 uncertainties are defined as relatively minor – as representing “a clear enough future” set within a “single system model” whereby outcomes can be estimated with reasonable accuracy;
- Level 2 uncertainties display “alternative futures” but, again, within a single system in which probability estimates can be applied with confidence.
Levels 3 and 4 uncertainties are described as representing “deep uncertainty”.
- Level 3 uncertainties are described as “a multiplicity of plausible futures”, in which multiple systems interact, but in which we can identify “a known range of outcomes”
- Level 4 uncertainties lead us to an “unknown future” in which we don’t understand the system: we know only that there is something, or are some things, that we know we don’t know.
This hierarchy can be useful to help us think carefully about whether the uncertainty behind a problem can be defined in terms of a Level 1 prediction, with parameters for variation. Or, can it be resolved as group of Level 2 possibilities with probability estimates for each? Can the issue only be understood as a set of different Level 3 futures, each with a clear set of defined outcomes, or only by means of a Level 4 statement to the effect that we know only that there is something crucial that we don’t yet know?
There is often no clear or unanimous view of whether a particular uncertainty is set at a specific level. Uncertainty should always be considered at the deepest proposed level, unless or until those that propose this level can be convinced by an evidence-based argument that it should be otherwise.
- Walker, W.E., Marchau, V.A.W.J. and Swanson, D. (2010) “Addressing Deep Uncertainty using Adaptive Policies: Introduction to Section 2”, Technological Forecasting & Social Change, 77: 917–23.
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