 Robust Decisions under Risk for Imprecise ProbabilitiesWłodzimierz Ogryczak ()
A chapter in Managing Safety of Heterogeneous Systems, 2012, pp 51-66 from Springer Abstract: Abstract In this paper we analyze robust approaches to decision making under uncertainty where the expected outcome is maximized but the probabilities are known imprecisely. A conservative robust approach takes into account any probability distribution thus leading to the notion of robustness focusing on the worst case scenario and resulting in the max-min optimization. We consider softer robust models allowing the probabilities to vary only within given intervals. We show that the robust solution for only upper bounded probabilities becomes the tail mean, known also as the conditional value-at-risk (CVaR), with an appropriate tolerance level. For proportional upper and lower probability limits the corresponding robust solution may be expressed by the optimization of appropriately combined the mean and the tail mean criteria. Finally, a general robust solution for any arbitrary intervals of probabilities can be expressed with the optimization problem very similar to the tail mean and thereby easily implementable with auxiliary linear inequalities. Date: 2012 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-642-22884-1_3 Ordering information: This item can be ordered from DOI: 10.1007/978-3-642-22884-1_3 Access Statistics for this chapter More chapters in Lecture Notes in Economics and Mathematical Systems from Springer |
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