▁be ▁difficult ▁even ▁estim ating ▁whether ▁ ▁is ▁more ▁likely ▁than ▁. ▁ ▁Nevertheless , ▁method olog ically ▁speaking , ▁this ▁difficulty ▁is ▁not ▁as ▁problem atic ▁as ▁ ▁bas ing ▁the ▁analysis ▁of ▁a ▁problem ▁subject ▁to ▁severe ▁uncertainty ▁on ▁a ▁single ▁point ▁estimate ▁and ▁its ▁immediate ▁neighborhood , ▁as ▁done ▁by ▁info - g ap . ▁And ▁what ▁is ▁more , ▁contrary ▁to ▁info - g ap , ▁this ▁approach ▁is ▁global , ▁rather ▁than ▁local . ▁ ▁Still , ▁it ▁must ▁be ▁str essed ▁that ▁Bay esian ▁analysis ▁does ▁not ▁express ly ▁concern ▁itself ▁with ▁the ▁question ▁of ▁robust ness . ▁ ▁Bay esian ▁analysis ▁ra ises ▁the ▁issue ▁of ▁learning ▁from ▁experience ▁and ▁adjust ing ▁prob abilities ▁accordingly . ▁In ▁other ▁words , ▁decision ▁is ▁not ▁a ▁one - stop ▁process , ▁but ▁prof its ▁from ▁a ▁sequence ▁of ▁dec isions ▁and ▁observations . ▁ ▁Class ical ▁decision ▁theory ▁perspective ▁ ▁Sn ied ov ich ▁ra ises ▁two ▁ob jections ▁to ▁info - g ap ▁decision ▁theory , ▁from ▁the ▁point ▁of ▁view ▁of ▁classical ▁decision ▁theory , ▁one ▁substant ive , ▁one ▁scholar ly : ▁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 ▁situations ▁of ▁" se vere ▁uncertainty ". ▁Sn ied ov ich ▁arg ues ▁that ▁under ▁severe ▁uncertainty , ▁one ▁should ▁not ▁start ▁from ▁a ▁point