▁estimate , ▁which ▁is ▁assumed ▁to ▁be ▁seriously ▁fla wed : ▁instead ▁the ▁set ▁one ▁should ▁consider ▁is ▁the ▁universe ▁of ▁possibilities , ▁not ▁subsets ▁there of . ▁St ated ▁altern atively , ▁under ▁severe ▁uncertainty , ▁one ▁should ▁use ▁global ▁decision ▁theory ▁( cons ider ▁the ▁entire ▁universe ), ▁not ▁local ▁decision ▁theory ▁( start ing ▁with ▁an ▁estimate ▁and ▁considering ▁devi ations ▁from ▁it ). ▁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 ". ▁ ▁Sn ied ov ich ▁has ▁challeng ed ▁the ▁valid ity ▁of ▁info - g ap ▁theory ▁for ▁making ▁dec isions ▁under ▁severe ▁uncertainty . ▁He ▁questions ▁the ▁effect iveness ▁of ▁info - g ap ▁theory ▁in ▁situations ▁where ▁the ▁best ▁estimate ▁ ▁is ▁a ▁poor ▁indic ation ▁of ▁the ▁true ▁value ▁of ▁. ▁Sn ied ov ich ▁notes ▁that ▁the ▁info - g ap ▁robust