, ▁while ▁Sn ied ov ich ▁cons iders ▁it ▁a ▁case ▁of ▁maxim in . ▁ ▁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 ness ▁function ▁is ▁" local " ▁to ▁the ▁region ▁around ▁, ▁where ▁ ▁is ▁likely ▁to ▁be ▁substantial ly ▁in ▁error . ▁He ▁con cludes ▁that ▁therefore ▁the ▁info - g ap ▁robust ness ▁function ▁is ▁an ▁un re li able ▁assess ment ▁of ▁imm unity ▁to ▁error . ▁ ▁Max imin ▁ ▁Sn ied ov ich ▁arg ues ▁that ▁info - g ap ' s ▁robust ness ▁model ▁is ▁maxim in ▁analysis ▁of , ▁not ▁the ▁outcome , ▁but ▁the ▁horizon ▁of ▁uncertainty : ▁it ▁cho oses ▁an ▁estimate ▁such ▁that ▁one ▁maxim izes ▁the ▁horizon ▁of ▁uncertainty ▁ ▁such ▁that ▁the ▁minimal ▁( crit ical ) ▁outcome ▁is ▁achieved , ▁assuming ▁worst - case ▁outcome ▁for ▁a ▁particular ▁horizon . ▁Symbol ically , ▁max ▁ ▁assuming ▁min ▁( wor st - case ) ▁outcome , ▁or ▁maxim in . ▁ ▁In ▁other ▁words , ▁while ▁it ▁is ▁not ▁a ▁maxim in ▁analysis ▁of ▁outcome ▁over ▁the ▁universe ▁of ▁uncertainty , ▁it ▁is ▁a ▁maxim in ▁analysis ▁over ▁a ▁properly ▁constru