▁situations ▁of ▁" se vere ▁uncertainty ". ▁Sn ied ov ich ▁arg ues ▁that ▁under ▁severe ▁uncertainty , ▁one ▁should ▁not ▁start ▁from ▁a ▁point ▁estimate , ▁which ▁is ▁likely ▁to ▁be ▁seriously ▁fla wed . ▁Instead , ▁one ▁should ▁consider ▁the ▁universe ▁of ▁possibilities , ▁not ▁its ▁subsets . ▁St ated ▁altern atively , ▁under ▁severe ▁uncertainty , ▁one ▁should ▁use ▁global ▁decision ▁theory ▁( cons ider ▁the ▁entire ▁region ▁of ▁uncertainty ), ▁not ▁local ▁decision ▁theory ▁( start ing ▁with ▁a ▁point ▁estimate ▁and ▁considering ▁devi ations ▁from ▁it ). ▁Sn ied ov ich ▁arg ues ▁that ▁info - g ap ▁decision ▁theory ▁is ▁therefore ▁a ▁" vo od oo ▁decision ▁theory ." ▁ 2 . ▁info - g ap ▁is ▁maxim in ▁Ben - Ha im ▁( 2 0 0 6 , ▁p . x ii ) ▁claims ▁that ▁info - g ap ▁is ▁" rad ically ▁different ▁from ▁all ▁current ▁theories ▁of ▁decision ▁under ▁uncertainty ," ▁while ▁Sn ied ov ich ▁arg ues ▁that ▁info - g ap ' s ▁robust ness ▁analysis ▁is ▁precisely ▁maxim in ▁analysis ▁of ▁the ▁horizon ▁of ▁uncertainty . ▁By ▁contrast , ▁Ben - Ha im ▁states ▁( Ben - Ha im ▁ 1 9 9 9 , ▁pp .   2 7 1 – 2 ) ▁that ▁" rob ust ▁reli ability ▁is ▁em ph atically ▁not ▁a ▁[ min - max ] ▁worst - case ▁analysis ". ▁Note ▁that ▁Ben - Ha im ▁comp ares ▁info - g ap ▁to ▁minim ax