, ▁and ▁out comes ▁for ▁these ▁distant ▁points ▁are ▁significantly ▁different ▁from ▁points ▁near ▁the ▁estimate , ▁then ▁conclus ions ▁of ▁robust ness ▁or ▁opportun eness ▁anal ys es ▁will ▁generally ▁be : ▁" one ▁must ▁be ▁very ▁confident ▁of ▁one ' s ▁assumptions , ▁else ▁out comes ▁may ▁be ▁expected ▁to ▁vary ▁significantly ▁from ▁pro jections " ▁– ▁a ▁ca ution ary ▁conclusion . ▁ ▁Disc la imer ▁and ▁summary ▁The ▁robust ness ▁and ▁opportun eness ▁functions ▁can ▁inform ▁decision . ▁For ▁example , ▁a ▁change ▁in ▁decision ▁increasing ▁robust ness ▁may ▁increase ▁or ▁decrease ▁opportun eness . ▁From ▁a ▁subject ive ▁st ance , ▁robust ness ▁and ▁opportun eness ▁both ▁trade - off ▁against ▁asp iration ▁for ▁outcome : ▁robust ness ▁and ▁opportun eness ▁d eter ior ate ▁as ▁the ▁decision ▁mak er ' s ▁asp ir ations ▁increase . ▁Rob ust ness ▁is ▁zero ▁for ▁model - best ▁anticip ated ▁out comes . ▁Rob ust ness ▁curves ▁for ▁alternative ▁dec isions ▁may ▁cross ▁as ▁a ▁function ▁of ▁asp iration , ▁imp lying ▁revers al ▁of ▁preference . ▁ ▁Vari ous ▁the or ems ▁identify ▁conditions ▁where ▁larger ▁info - g ap ▁robust ness ▁implies ▁larger ▁probability ▁of ▁success , ▁regardless ▁of ▁the ▁underlying ▁probability ▁distribution . ▁However , ▁these ▁conditions ▁are ▁technical , ▁and ▁do ▁not ▁translate ▁into ▁any ▁common - s ense , ▁ver bal ▁recommend ations , ▁limit ing ▁such ▁applications ▁of ▁info - g ap ▁theory ▁by ▁non - ex per ts . ▁ ▁Crit ic ism