▁used ▁for ▁satisf icing , ▁as ▁an ▁alternative ▁to ▁optim izing ▁in ▁the ▁presence ▁of ▁uncertainty ▁or ▁bounded ▁rational ity ; ▁see ▁robust ▁optimization ▁for ▁an ▁alternative ▁approach . ▁ ▁Com parison ▁with ▁classical ▁decision ▁theory ▁▁ ▁In ▁contrast ▁to ▁probabil istic ▁decision ▁theory , ▁info - g ap ▁analysis ▁does ▁not ▁use ▁probability ▁distributions : ▁it ▁measures ▁the ▁deviation ▁of ▁errors ▁( d iffer ences ▁between ▁the ▁parameter ▁and ▁the ▁estimate ), ▁but ▁not ▁the ▁probability ▁of ▁out comes ▁– ▁in ▁particular , ▁the ▁estimate ▁ ▁is ▁in ▁no ▁sense ▁more ▁or ▁less ▁likely ▁than ▁other ▁points , ▁as ▁info - g ap ▁does ▁not ▁use ▁probability . ▁Info - g ap , ▁by ▁not ▁using ▁probability ▁distributions , ▁is ▁robust ▁in ▁that ▁it ▁is ▁not ▁sensitive ▁to ▁assumptions ▁on ▁prob abilities ▁of ▁out comes . ▁However , ▁the ▁model ▁of ▁uncertainty ▁does ▁include ▁a ▁notion ▁of ▁" clos er " ▁and ▁" more ▁distant " ▁out comes , ▁and ▁thus ▁includes ▁some ▁assumptions , ▁and ▁is ▁not ▁as ▁robust ▁as ▁simply ▁considering ▁all ▁possible ▁out comes , ▁as ▁in ▁minim ax . ▁Further , ▁it ▁cons iders ▁a ▁fixed ▁universe ▁ ▁so ▁it ▁is ▁not ▁robust ▁to ▁unexpected ▁( not ▁mode led ) ▁events . ▁ ▁The ▁connection ▁to ▁minim ax ▁analysis ▁has ▁occasion ed ▁some ▁controvers y : ▁( Ben - Ha im ▁ 1 9 9 9 , ▁pp .   2 7 1 – 2 ) ▁arg ues ▁that ▁info - g ap ' s ▁robust ness ▁analysis