for ese en ▁events ▁( th ose ▁not ▁in ▁the ▁universe ▁) ▁are ▁not ▁incorpor ated : ▁info - g ap ▁addresses ▁mode led ▁uncertainty , ▁not ▁unexpected ▁uncertainty , ▁as ▁in ▁black ▁sw an ▁theory , ▁particularly ▁the ▁lud ic ▁f alla cy . ▁This ▁is ▁not ▁a ▁problem ▁when ▁the ▁possible ▁events ▁by ▁definition ▁fall ▁in ▁a ▁given ▁universe , ▁but ▁in ▁real ▁world ▁applications , ▁significant ▁events ▁may ▁be ▁" out side ▁model ". ▁For ▁instance , ▁a ▁simple ▁model ▁of ▁daily ▁stock ▁market ▁returns ▁– ▁which ▁by ▁definition ▁fall ▁in ▁the ▁range ▁ ▁– ▁may ▁include ▁extreme ▁moves ▁such ▁as ▁Black ▁Monday ▁( 1 9 8 7 ) ▁but ▁might ▁not ▁model ▁the ▁market ▁break down s ▁following ▁the ▁September ▁ 1 1 ▁attacks : ▁it ▁cons iders ▁the ▁" known ▁unknown s ", ▁not ▁the ▁" unknown ▁unknown s ". ▁This ▁is ▁a ▁general ▁criticism ▁of ▁much ▁decision ▁theory , ▁and ▁is ▁by ▁no ▁means ▁specific ▁to ▁info - g ap , ▁but ▁info - g ap ▁is ▁not ▁imm une ▁to ▁it . ▁ ▁Second ly , ▁there ▁is ▁no ▁natural ▁scale : ▁is ▁uncertainty ▁of ▁ ▁small ▁or ▁large ? ▁D ifferent ▁models ▁of ▁uncertainty ▁give ▁different ▁scales , ▁and ▁require ▁judgment ▁and ▁understanding ▁of ▁the ▁domain ▁and ▁the ▁model ▁of ▁uncertainty . ▁Similarly , ▁meas uring ▁differences ▁between ▁out comes ▁requires ▁judgment ▁and ▁understanding ▁of ▁the ▁domain . ▁ ▁Third ly , ▁if ▁the ▁universe ▁under ▁consideration ▁is ▁larger ▁than ▁a ▁significant ▁horizon ▁of ▁uncertainty