▁( e . g . ▁Ben - Ha im ▁ 2 0 0 1 , ▁ 2 0 0 6 ) ▁as ▁a ▁new ▁non - prob abil istic ▁theory ▁that ▁is ▁rad ically ▁different ▁from ▁all ▁current ▁decision ▁theories ▁for ▁decision ▁under ▁uncertainty . ▁So , ▁it ▁is ▁imper ative ▁to ▁examine ▁in ▁this ▁discussion ▁ ▁in ▁what ▁way , ▁if ▁any , ▁is ▁info - g ap ' s ▁robust ness ▁model ▁rad ically ▁different ▁from ▁Max imin . ▁For ▁one ▁thing , ▁there ▁is ▁a ▁well - est ab lished ▁assess ment ▁of ▁the ▁utility ▁of ▁Max imin . ▁For ▁example , ▁Ber ger ▁( Ch apter ▁ 5 ) ▁suggests ▁that ▁even ▁in ▁situations ▁where ▁no ▁prior ▁information ▁is ▁available ▁( a ▁best ▁case ▁for ▁Max imin ), ▁Max imin ▁can ▁lead ▁to ▁bad ▁decision ▁rules ▁and ▁be ▁hard ▁to ▁implement . ▁He ▁recomm ends ▁Bay esian ▁method ology . ▁And ▁as ▁indicated ▁above , ▁It ▁should ▁also ▁be ▁remarked ▁that ▁the ▁minim ax ▁principle ▁even ▁if ▁it ▁is ▁applicable ▁leads ▁to ▁an ▁extremely ▁conserv ative ▁policy . ▁T int ner ▁( 1 9 5 2 , ▁p .   2 5 ) ▁▁▁ ▁However , ▁quite ▁apart ▁from ▁the ▁ram ifications ▁that ▁establish ing ▁this ▁point ▁might ▁have ▁for ▁the ▁utility ▁of ▁info - g aps ' ▁robust ness ▁model , ▁the ▁reason ▁that ▁it ▁beho ov es ▁us ▁to ▁clarify ▁the ▁relationship ▁between ▁info - g ap ▁and ▁Max imin ▁is ▁the ▁central ity ▁of ▁the ▁latter