. ▁A ▁number ▁of ▁authors ▁have ▁noted ▁and ▁discussed ▁similar ities ▁and ▁differences ▁between ▁info - g ap ▁robust ness ▁and ▁minim ax ▁or ▁worst - case ▁methods ▁▁▁▁▁▁▁▁ ▁. ▁▁▁ ▁Sn ied ov ich ▁▁▁▁ ▁has ▁demonstrated ▁formally ▁that ▁the ▁info - g ap ▁robust ness ▁function ▁can ▁be ▁represented ▁as ▁a ▁maxim in ▁optimization , ▁and ▁is ▁thus ▁related ▁to ▁Wald ' s ▁minim ax ▁theory . ▁Sn ied ov ich ▁ ▁has ▁claimed ▁that ▁info - g ap ' s ▁robust ness ▁analysis ▁is ▁conducted ▁in ▁the ▁neighborhood ▁of ▁an ▁estimate ▁that ▁is ▁likely ▁to ▁be ▁substantial ly ▁wrong , ▁con cluding ▁that ▁the ▁resulting ▁robust ness ▁function ▁is ▁equally ▁likely ▁to ▁be ▁substantial ly ▁wrong . ▁▁▁▁▁▁▁▁ ▁On ▁the ▁other ▁hand , ▁the ▁estimate ▁is ▁the ▁best ▁one ▁has , ▁so ▁it ▁is ▁useful ▁to ▁know ▁if ▁it ▁can ▁err ▁greatly ▁and ▁still ▁yield ▁an ▁acceptable ▁outcome . ▁This ▁critical ▁question ▁clearly ▁ra ises ▁the ▁issue ▁of ▁whether ▁robust ness ▁( as ▁defined ▁by ▁info - g ap ▁theory ) ▁is ▁qualified ▁to ▁judge ▁whether ▁confidence ▁is ▁war r anted , ▁▁▁▁▁ ▁and ▁how ▁it ▁comp ares ▁to ▁methods ▁used ▁to ▁inform ▁dec isions ▁under ▁uncertainty ▁using ▁consider ations ▁not ▁limited ▁to ▁the ▁neighborhood ▁of ▁a ▁bad ▁initial ▁guess . ▁An swers ▁to ▁these ▁questions ▁vary ▁with ▁the ▁particular ▁problem ▁at ▁hand . ▁Some ▁general ▁comments ▁follow . ▁ ▁S ens itivity ▁analysis ▁▁ ▁S ens itivity ▁analysis ▁– ▁how ▁sensitive ▁conclus ions ▁are ▁to ▁input ▁assumptions ▁– ▁can