▁ ▁A ▁general ▁criticism ▁of ▁non - prob abil istic ▁decision ▁rules , ▁discussed ▁in ▁detail ▁at ▁decision ▁theory : ▁alternatives ▁to ▁probability ▁theory , ▁is ▁that ▁optimal ▁decision ▁rules ▁( form ally , ▁adm iss ible ▁decision ▁rules ) ▁can ▁always ▁be ▁derived ▁by ▁probabil istic ▁methods , ▁with ▁a ▁suitable ▁utility ▁function ▁and ▁prior ▁distribution ▁( this ▁is ▁the ▁statement ▁of ▁the ▁complete ▁class ▁the or ems ), ▁and ▁thus ▁that ▁non - prob abil istic ▁methods ▁such ▁as ▁info - g ap ▁are ▁unnecessary ▁and ▁do ▁not ▁yield ▁new ▁or ▁better ▁decision ▁rules . ▁ ▁A ▁more ▁general ▁criticism ▁of ▁decision ▁making ▁under ▁uncertainty ▁is ▁the ▁impact ▁of ▁out s ized , ▁unexpected ▁events , ▁ones ▁that ▁are ▁not ▁captured ▁by ▁the ▁model . ▁This ▁is ▁discussed ▁particularly ▁in ▁black ▁sw an ▁theory , ▁and ▁info - g ap , ▁used ▁in ▁isol ation , ▁is ▁vulner able ▁to ▁this , ▁as ▁are ▁a ▁fort ior i ▁all ▁decision ▁theories ▁that ▁use ▁a ▁fixed ▁universe ▁of ▁possibilities , ▁not ably ▁probabil istic ▁ones . ▁ ▁In ▁criticism ▁specific ▁to ▁info - g ap , ▁Sn ied ov ich ▁ra ises ▁two ▁ob jections ▁to ▁info - g ap ▁decision ▁theory , ▁one ▁substant ive , ▁one ▁scholar ly : ▁ 1 . ▁the ▁info - g ap ▁uncertainty ▁model ▁is ▁fla wed ▁and ▁o vers old ▁Info - g ap ▁models ▁uncertainty ▁via ▁a ▁nested ▁family ▁of ▁subsets ▁around ▁a ▁point ▁estimate , ▁and ▁is ▁tout ed ▁as ▁applicable ▁under