▁wrong , ▁we ▁have ▁no ▁other ▁choice — method olog ically ▁speaking — but ▁to ▁assume ▁that ▁the ▁results ▁generated ▁by ▁this ▁analysis ▁are ▁equally ▁likely ▁to ▁be ▁substantial ly ▁wrong . ▁In ▁other ▁words , ▁in ▁accord ance ▁with ▁the ▁universal ▁Gar bage ▁In ▁- ▁Gar bage ▁Out ▁A xi om , ▁we ▁have ▁to ▁assume ▁that ▁the ▁quality ▁of ▁the ▁results ▁generated ▁by ▁info - g ap ' s ▁analysis ▁is ▁only ▁as ▁good ▁as ▁the ▁quality ▁of ▁the ▁estimate ▁on ▁which ▁the ▁results ▁are ▁based . ▁ ▁The ▁picture ▁speak s ▁for ▁itself . ▁ ▁What ▁emer ges ▁then ▁is ▁that ▁info - g ap ▁theory ▁is ▁yet ▁to ▁explain ▁in ▁what ▁way , ▁if ▁any , ▁ ▁it ▁actually ▁attempts ▁to ▁deal ▁with ▁the ▁sever ity ▁of ▁the ▁uncertainty ▁under ▁consideration . ▁Sub sequ ent ▁sections ▁of ▁this ▁article ▁will ▁address ▁this ▁sever ity ▁issue ▁and ▁its ▁method ological ▁and ▁practical ▁imp lications . ▁ ▁A ▁more ▁detailed ▁analysis ▁of ▁an ▁illustr ative ▁numerical ▁invest ment ▁problem ▁of ▁this ▁type ▁can ▁be ▁found ▁in ▁Sn ied ov ich ▁( 2 0 0 7 ). ▁ ▁Un certain ty ▁models ▁ ▁Info - g aps ▁are ▁quant ified ▁by ▁info - g ap ▁models ▁of ▁uncertainty . ▁An ▁info - g ap ▁model ▁is ▁an ▁un bounded ▁family ▁of ▁nested ▁sets . ▁For ▁example , ▁a ▁frequently ▁encountered ▁example ▁is ▁a ▁family ▁of ▁nested ▁elli ps oid s ▁all ▁having ▁the ▁same ▁shape . ▁The ▁structure ▁of ▁the ▁sets ▁in