On solving decision and risk management problems subject to uncertainty. (arXiv:2301.10244v1 [cs.AI])

Uncertainty is a pervasive challenge in decision and risk management and it
is usually studied by quantification and modeling. Interestingly, engineers and
other decision makers usually manage uncertainty with strategies such as
incorporating robustness, or by employing decision heuristics. The focus of
this paper is then to develop a systematic understanding of such strategies,
determine their range of application, and develop a framework to better employ
them.

Based on a review of a dataset of 100 decision problems, this paper found
that many decision problems have pivotal properties, i.e. properties that
enable solution strategies, and finds 14 such properties. Therefore, an analyst
can first find these properties in a given problem, and then utilize the
strategies they enable. Multi-objective optimization methods could be used to
make investment decisions quantitatively. The analytical complexity of decision
problems can also be scored by evaluating how many of the pivotal properties
are available. Overall, we find that in the light of pivotal properties,
complex problems under uncertainty frequently appear surprisingly tractable.