ing ▁the ▁sever ity ▁of ▁the ▁uncertainty ▁that ▁info - g ap ▁was ▁designed ▁to ▁tack le , ▁it ▁is ▁essential ▁to ▁clarify ▁the ▁difficulties ▁pos ed ▁by ▁severe ▁uncertainty . ▁ ▁Since ▁info - g ap ▁is ▁a ▁non - prob abil istic ▁method ▁that ▁see ks ▁to ▁maxim ize ▁robust ness ▁to ▁uncertainty , ▁it ▁is ▁imper ative ▁to ▁compare ▁it ▁to ▁the ▁single ▁most ▁important ▁" non - prob abil istic " ▁model ▁in ▁classical ▁decision ▁theory , ▁namely ▁Wald ' s ▁Max imin ▁parad ig m ▁( W ald ▁ 1 9 4 5 , ▁ 1 9 5 0 ). ▁ ▁After ▁all , ▁this ▁parad ig m ▁has ▁domin ated ▁the ▁scene ▁in ▁classical ▁decision ▁theory ▁for ▁well ▁over ▁si xty ▁years ▁now . ▁ ▁So , ▁first ▁let ▁us ▁clarify ▁the ▁assumptions ▁that ▁are ▁imp lied ▁by ▁severe ▁uncertainty . ▁ ▁Working ▁assumptions ▁▁ ▁Info - g ap ▁decision ▁theory ▁emp lo ys ▁three ▁simple ▁construct s ▁to ▁capture ▁the ▁uncertainty ▁associated ▁with ▁decision ▁problems : ▁ ▁A ▁parameter ▁ ▁whose ▁true ▁value ▁is ▁subject ▁to ▁severe ▁uncertainty . ▁ ▁A ▁region ▁of ▁uncertainty ▁ ▁where ▁the ▁true ▁value ▁of ▁ ▁lies . ▁ ▁An ▁estimate ▁ ▁of ▁the ▁true ▁value ▁of ▁. ▁ ▁It ▁should ▁be ▁pointed ▁out , ▁though , ▁that ▁as ▁such ▁these ▁construct s ▁are ▁generic , ▁meaning ▁that ▁they ▁can ▁be ▁employed ▁to ▁model ▁situations ▁where ▁the ▁uncertainty ▁is ▁not ▁severe ▁but ▁m ild , ▁indeed ▁very ▁m ild . ▁