▁a ▁parameter : ▁with ▁no ▁uncertainty , ▁the ▁estimate ▁is ▁correct , ▁and ▁as ▁uncertainty ▁increases , ▁the ▁subset ▁grows , ▁in ▁general ▁without ▁bound . ▁The ▁subsets ▁quant ify ▁uncertainty ▁– ▁the ▁horizon ▁of ▁uncertainty ▁measures ▁the ▁" distance " ▁between ▁an ▁estimate ▁and ▁a ▁possibility ▁– ▁providing ▁an ▁intermediate ▁measure ▁between ▁a ▁single ▁point ▁( the ▁point ▁estimate ) ▁and ▁the ▁universe ▁of ▁all ▁possibilities , ▁and ▁giving ▁a ▁measure ▁for ▁sens itivity ▁analysis : ▁how ▁uncertain ▁can ▁an ▁estimate ▁be ▁and ▁a ▁decision ▁( based ▁on ▁this ▁incorrect ▁estimate ) ▁still ▁yield ▁an ▁acceptable ▁outcome ▁– ▁what ▁is ▁the ▁margin ▁of ▁error ? ▁ ▁Info - g ap ▁is ▁a ▁local ▁decision ▁theory , ▁beginning ▁with ▁an ▁estimate ▁and ▁considering ▁devi ations ▁from ▁it ; ▁this ▁contrast s ▁with ▁global ▁methods ▁such ▁as ▁minim ax , ▁which ▁cons iders ▁worst - case ▁analysis ▁over ▁the ▁entire ▁space ▁of ▁out comes , ▁and ▁probabil istic ▁decision ▁theory , ▁which ▁cons iders ▁all ▁possible ▁out comes , ▁and ▁assign s ▁some ▁probability ▁to ▁them . ▁In ▁info - g ap , ▁the ▁universe ▁of ▁possible ▁out comes ▁under ▁consideration ▁is ▁the ▁union ▁of ▁all ▁of ▁the ▁nested ▁subsets : ▁▁ ▁Info - g ap ▁analysis ▁gives ▁answers ▁to ▁such ▁questions ▁as : ▁ ▁under ▁what ▁level ▁of ▁uncertainty ▁can ▁specific ▁requirements ▁be ▁reli ably ▁assured ▁( rob ust ness ), ▁and ▁ ▁what ▁level ▁of ▁uncertainty ▁is ▁necessary ▁to ▁achieve ▁certain ▁wind falls ▁( o pport un eness ). ▁It ▁can ▁be