ness ▁of ▁decision ▁ ▁is ▁based ▁on ▁its ▁( wor st - case ) ▁performance ▁over ▁no ▁more ▁than ▁a ▁min usc ule ▁part ▁of ▁the ▁total ▁region ▁of ▁uncertainty ▁that ▁is ▁an ▁immediate ▁neighborhood ▁of ▁the ▁estimate ▁. ▁Since ▁usually ▁info - g ap ' s ▁total ▁region ▁of ▁uncertainty ▁is ▁un bounded , ▁this ▁illustr ation ▁represents ▁a ▁usual ▁  ▁case ▁rather ▁than ▁an ▁exception . ▁ ▁Info - g ap ' s ▁robust ness / o pport un eness ▁are ▁by ▁definition ▁local ▁properties . ▁ ▁As ▁such ▁they ▁cannot ▁assess ▁the ▁performance ▁of ▁dec isions ▁over ▁the ▁total ▁region ▁of ▁uncertainty . ▁For ▁this ▁reason ▁it ▁is ▁not ▁clear ▁how ▁Info - G ap ' s ▁Rob ust ness / O pport un eness ▁models ▁can ▁provide ▁a ▁meaning ful / sound / use ful ▁basis ▁for ▁decision ▁under ▁severe ▁uncertainty ▁where ▁the ▁estimate ▁is ▁poor ▁and ▁is ▁likely ▁to ▁be ▁substantial ly ▁wrong . ▁ ▁This ▁cru cial ▁issue ▁is ▁addressed ▁in ▁subsequent ▁sections ▁of ▁this ▁article . ▁ ▁Max imin / Min imin : ▁playing ▁robust ness / o pport un eness ▁games ▁with ▁Nature ▁▁ ▁For ▁well ▁over ▁si xty ▁years ▁now ▁ ▁Wald ' s ▁ ▁Max imin ▁ ▁model ▁has ▁figured ▁in ▁classical ▁decision ▁theory ▁and ▁related ▁areas ▁– ▁such ▁as ▁robust ▁optimization ▁- ▁as ▁the ▁for em ost ▁non - prob abil istic ▁parad ig m ▁for ▁model ing ▁and ▁treatment ▁of ▁severe ▁uncertainty . ▁ ▁Info - g ap ▁is ▁prop ounded