▁Sub sequ ent ▁sections ▁of ▁this ▁article ▁describe ▁various ▁aspects ▁of ▁info - g ap ▁decision ▁theory ▁and ▁its ▁applications , ▁how ▁it ▁ ▁propos es ▁to ▁co pe ▁with ▁the ▁working ▁assumptions ▁out lined ▁above , ▁the ▁local ▁nature ▁of ▁info - g ap ' s ▁robust ness ▁analysis ▁and ▁its ▁int imate ▁ ▁relationship ▁with ▁Wald ' s ▁classical ▁Max imin ▁parad ig m ▁and ▁worst - case ▁analysis . ▁ ▁In var iance ▁property ▁▁ ▁The ▁main ▁point ▁to ▁keep ▁in ▁mind ▁here ▁is ▁that ▁info - g ap ' s ▁raison ▁d ' être ▁is ▁to ▁provide ▁a ▁method ology ▁for ▁decision ▁under ▁severe ▁uncertainty . ▁ ▁This ▁means ▁that ▁its ▁primary ▁test ▁would ▁be ▁in ▁the ▁effic acy ▁of ▁its ▁handling ▁of ▁and ▁cop ing ▁with ▁ ▁severe ▁uncertainty . ▁ ▁To ▁this ▁end ▁it ▁must ▁be ▁established ▁first ▁how ▁Info - G ap ' s ▁robust ness / o pport un eness ▁models ▁behave / fare , ▁as ▁the ▁sever ity ▁of ▁the ▁uncertainty ▁is ▁increased / de cre ased . ▁ ▁Second , ▁it ▁must ▁be ▁established ▁whether ▁info - g ap ' s ▁robust ness / o pport un eness ▁models ▁give ▁ade qu ate ▁expression ▁to ▁the ▁potential ▁vari ability ▁of ▁the ▁performance ▁function ▁over ▁the ▁entire ▁region ▁of ▁uncertainty . ▁ ▁This ▁is ▁particularly ▁important ▁because ▁Info — G ap ▁is ▁usually ▁concerned ▁with ▁relatively ▁large , ▁indeed ▁un bounded , ▁regions ▁of ▁uncertainty . ▁ ▁So , ▁let ▁ ▁denote ▁the ▁total