ed ▁decision ▁space . ▁ ▁Ben - Ha im ▁arg ues ▁that ▁info - g ap ' s ▁robust ness ▁model ▁is ▁not ▁min - max / maxim in ▁analysis ▁because ▁it ▁is ▁not ▁worst - case ▁analysis ▁of ▁out comes ; ▁it ▁is ▁a ▁satisf icing ▁model , ▁not ▁an ▁optimization ▁model ▁– ▁a ▁( stra ight forward ) ▁maxim in ▁analysis ▁would ▁consider ▁worst - case ▁out comes ▁over ▁the ▁entire ▁space ▁which , ▁since ▁uncertainty ▁is ▁often ▁potentially ▁un bounded , ▁would ▁yield ▁an ▁un bounded ▁bad ▁worst ▁case . ▁ ▁St ability ▁radius ▁▁ ▁Sn ied ov ich ▁has ▁shown ▁that ▁info - g ap ' s ▁robust ness ▁model ▁is ▁a ▁simple ▁stability ▁radius ▁model , ▁namely ▁a ▁local ▁stability ▁model ▁of ▁the ▁generic ▁form ▁ ▁where ▁ ▁denotes ▁a ▁ball ▁of ▁radius ▁ ▁centered ▁at ▁ ▁and ▁ ▁denotes ▁the ▁set ▁of ▁values ▁of ▁ ▁that ▁satisfy ▁ ▁pre - det erm ined ▁stability ▁conditions . ▁ ▁In ▁other ▁words , ▁info - g ap ' s ▁robust ness ▁model ▁is ▁a ▁stability ▁radius ▁model ▁character ized ▁by ▁a ▁stability ▁requirement ▁of ▁the ▁form ▁. ▁Since ▁stability ▁radius ▁models ▁are ▁designed ▁for ▁the ▁analysis ▁of ▁small ▁perturb ations ▁in ▁a ▁given ▁nom inal ▁value ▁of ▁a ▁parameter , ▁Sn ied ov ich ▁arg ues ▁that ▁info - g ap ' s ▁robust ness ▁model ▁is ▁un su itable ▁for ▁the ▁treatment ▁of ▁severe ▁uncertainty ▁character ized ▁by ▁a ▁poor ▁estimate ▁and ▁a ▁vast ▁uncertainty ▁space . ▁