▁generic ▁maxim in ▁and ▁Min imin ▁models , ▁respectively , ▁the ▁description ▁of ▁info - g ap ▁as ▁an ▁ ▁instance ▁of ▁  ▁these ▁models ▁is ▁too ▁strong . ▁ ▁The ▁argument ▁put ▁forward ▁is ▁that ▁although ▁it ▁is ▁true ▁that ▁info - g ap ' s ▁robust ness ▁model ▁can ▁be ▁expressed ▁as ▁a ▁maxim in ▁model , ▁the ▁former ▁is ▁not ▁an ▁instance ▁of ▁the ▁latter . ▁ ▁This ▁ob jection ▁apparently ▁st ems ▁from ▁the ▁fact ▁that ▁any ▁optimization ▁problem ▁can ▁be ▁form ulated ▁as ▁a ▁maxim in ▁model ▁by ▁a ▁simple ▁employ ment ▁of ▁dummy   ▁variables . ▁That ▁is , ▁clearly ▁▁▁▁ ▁where ▁▁▁▁ ▁for ▁any ▁arbitrary ▁non - empty ▁set ▁. ▁ ▁The ▁point ▁of ▁this ▁ob jection ▁seems ▁to ▁be ▁that ▁we ▁are ▁running ▁the ▁risk ▁of ▁water ing ▁down ▁the ▁meaning ▁of ▁the ▁term ▁instance   ▁if ▁we ▁thus ▁cont end ▁that ▁any ▁minim ization ▁problem ▁is ▁an ▁instance ▁of ▁the ▁maxim in ▁model . ▁ ▁It ▁must ▁therefore ▁be ▁pointed ▁out ▁that ▁this ▁concern ▁is ▁utter ly ▁unw arr anted ▁in ▁the ▁case ▁of ▁the ▁info - g ap / maxim in / min imin ▁relation . ▁The ▁correspond ence ▁between ▁info - g ap ' s ▁robust ness ▁model ▁and ▁the ▁generic ▁maxim in ▁model ▁is ▁neither ▁cont riv ed ▁nor ▁is ▁it ▁form ulated ▁with ▁the ▁aid ▁of ▁dummy ▁objects . ▁The ▁correspond ence ▁is ▁immediate , ▁intuit ive , ▁and ▁comp elling ▁hence , ▁apt ly ▁described ▁by ▁the ▁term ▁