Decision Making under Deep Uncertainty by Unknown

Author:Unknown
Language: eng
Format: epub
ISBN: 9783030052522
Publisher: Springer International Publishing

Fig. 10.3Extrapolation of populations beyond 2012, by continent

IG robustness makes it possible to assess the extent to which a policy decision is affected by what may be unknown, even in the presence of sources of uncertainty that do not lend themselves to parametric representations such as probability distributions, polynomial chaos expansions, or intervals. Accounting for an uncertainty such as the gray region of Fig. 10.3 is challenging if a functional form is lacking. One might not know if the world’s population can be modeled as increasing or decreasing, or even if the trend can be portrayed as monotonic.

Figure 10.4 suggests one possibility to handle this challenge, whereby increasing levels of uncertainty, as indicated by the uncertainty spaces U(α1) (blue region) and U(α2) (green region), are defined around a nominal trend (red-dashed line). The uncertainty can be explored by selecting population values within these sets and without necessarily having to formulate a parametric representation (e.g., “population growth is exponential”) if policymakers are not willing to postulate such an assumption. The figure illustrates two values chosen in set U(α1) at year Y1, and three values selected in the larger-uncertainty set U(α2) at year Y2. This procedure would typically be implemented to assess if the policy objective is met as future populations deviate from the nominal trend in unknown ways.

Fig. 10.4Representing increasing levels of uncertainty for the world’s population

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