▁that ▁have ▁acc ru ed ▁in ▁this ▁area ▁of ▁decision ▁theory , ▁no ▁fully ▁satisf actory ▁general ▁method ology ▁is ▁available ▁to ▁date . ▁ ▁Now , ▁as ▁port rayed ▁in ▁the ▁info - g ap ▁literature , ▁Info - G ap ▁was ▁designed ▁express ly ▁as ▁a ▁method ology ▁for ▁solving ▁decision ▁problems ▁that ▁are ▁subject ▁to ▁severe ▁uncertainty . ▁ ▁And ▁what ▁is ▁more , ▁its ▁aim ▁is ▁to ▁seek ▁solutions ▁that ▁are ▁robust . ▁ ▁Thus , ▁to ▁have ▁a ▁clear ▁picture ▁of ▁info - g ap ' s ▁mod us ▁oper and i ▁and ▁its ▁role ▁and ▁place ▁in ▁decision ▁theory ▁and ▁robust ▁optimization , ▁it ▁is ▁imper ative ▁to ▁examine ▁it ▁within ▁this ▁context . ▁ ▁In ▁other ▁words , ▁it ▁is ▁necessary ▁to ▁establish ▁info - g ap ' s ▁relation ▁to ▁classical ▁decision ▁theory ▁and ▁robust ▁optimization . ▁ ▁To ▁this ▁end , ▁the ▁following ▁questions ▁must ▁be ▁addressed : ▁ ▁What ▁are ▁the ▁characteristics ▁of ▁decision ▁problems ▁that ▁are ▁subject ▁to ▁severe ▁uncertainty ? ▁ ▁What ▁difficulties ▁arise ▁in ▁the ▁mod elling ▁and ▁solution ▁of ▁such ▁problems ? ▁ ▁What ▁type ▁of ▁robust ness ▁is ▁sought ? ▁ ▁How ▁does ▁info - g ap ▁theory ▁address ▁these ▁issues ? ▁ ▁In ▁what ▁way ▁is ▁info - g ap ▁decision ▁theory ▁similar ▁to ▁and / or ▁different ▁from ▁other ▁theories ▁for ▁decision ▁under ▁uncertainty ? ▁ ▁Two ▁important ▁points ▁need ▁to ▁be ▁el uc id ated ▁in ▁this ▁regard ▁at ▁the ▁out set : ▁ ▁Consider