▁be ▁performed ▁independently ▁of ▁a ▁model ▁of ▁uncertainty : ▁most ▁simply , ▁one ▁may ▁take ▁two ▁different ▁assumed ▁values ▁for ▁an ▁input ▁and ▁comp ares ▁the ▁conclus ions . ▁From ▁this ▁perspective , ▁info - g ap ▁can ▁be ▁seen ▁as ▁a ▁technique ▁of ▁sens itivity ▁analysis , ▁though ▁by ▁no ▁means ▁the ▁only . ▁ ▁Rob ust ▁optimization ▁▁ ▁The ▁robust ▁optimization ▁literature ▁ ▁provides ▁methods ▁and ▁techniques ▁that ▁take ▁a ▁global ▁approach ▁to ▁robust ness ▁analysis . ▁These ▁methods ▁directly ▁address ▁decision ▁under ▁severe ▁uncertainty , ▁and ▁have ▁been ▁used ▁for ▁this ▁purpose ▁for ▁more ▁than ▁thirty ▁years ▁now . ▁Wald ' s ▁Max imin ▁model ▁is ▁the ▁main ▁instrument ▁used ▁by ▁these ▁methods . ▁▁▁▁▁▁▁ ▁The ▁principal ▁difference ▁between ▁the ▁Max imin ▁model ▁employed ▁by ▁info - g ap ▁and ▁the ▁various ▁Max imin ▁models ▁employed ▁by ▁robust ▁optimization ▁methods ▁is ▁in ▁the ▁manner ▁in ▁which ▁the ▁total ▁region ▁of ▁uncertainty ▁is ▁incorpor ated ▁in ▁the ▁robust ness ▁model . ▁Info - g ap ▁takes ▁a ▁local ▁approach ▁that ▁concentr ates ▁on ▁the ▁immediate ▁neighborhood ▁of ▁the ▁estimate . ▁In ▁sharp ▁contrast , ▁robust ▁optimization ▁methods ▁set ▁out ▁to ▁incorpor ate ▁in ▁the ▁analysis ▁the ▁entire ▁region ▁of ▁uncertainty , ▁or ▁at ▁least ▁an ▁ade qu ate ▁representation ▁there of . ▁In ▁fact , ▁some ▁of ▁these ▁methods ▁do ▁not ▁even ▁use ▁an ▁estimate . ▁ ▁Compar ative ▁analysis ▁ ▁Class ical ▁decision ▁theory , ▁offers ▁two ▁approaches ▁to ▁decision - making ▁under ▁severe ▁uncertainty , ▁namely ▁maxim in ▁and