▁moving ▁is ▁more ▁robust ▁and ▁less ▁opport une . ▁ ▁This ▁example ▁demonstr ates ▁the ▁sens itivity ▁of ▁analysis ▁to ▁the ▁model ▁of ▁uncertainty . ▁ ▁Info - g ap ▁models ▁ ▁Info - g ap ▁can ▁be ▁applied ▁to ▁spaces ▁of ▁functions ; ▁in ▁that ▁case ▁the ▁uncertain ▁parameter ▁is ▁a ▁function ▁ ▁with ▁estimate ▁ ▁and ▁the ▁nested ▁subsets ▁are ▁sets ▁of ▁functions . ▁One ▁way ▁to ▁describe ▁such ▁a ▁set ▁of ▁functions ▁is ▁by ▁requiring ▁values ▁of ▁u ▁to ▁be ▁close ▁to ▁values ▁of ▁ ▁for ▁all ▁x , ▁using ▁a ▁family ▁of ▁info - g ap ▁models ▁on ▁the ▁values . ▁ ▁For ▁example , ▁the ▁above ▁fraction ▁error ▁model ▁for ▁values ▁becomes ▁the ▁fraction al ▁error ▁model ▁for ▁functions ▁by ▁adding ▁a ▁parameter ▁x ▁to ▁the ▁definition : ▁ ▁More ▁generally , ▁if ▁ ▁is ▁a ▁family ▁of ▁info - g ap ▁models ▁of ▁values , ▁then ▁one ▁obtain s ▁an ▁info - g ap ▁model ▁of ▁functions ▁in ▁the ▁same ▁way : ▁ ▁Mot iv ation ▁▁ ▁It ▁is ▁common ▁to ▁make ▁dec isions ▁under ▁uncertainty . ▁What ▁can ▁be ▁done ▁to ▁make ▁good ▁( or ▁at ▁least ▁the ▁best ▁possible ) ▁dec isions ▁under ▁conditions ▁of ▁uncertainty ? ▁Info - g ap ▁robust ness ▁analysis ▁evalu ates ▁each ▁feas ible ▁decision ▁by ▁asking : ▁how ▁much ▁deviation ▁from ▁an ▁estimate ▁of ▁a ▁parameter ▁value , ▁function , ▁or ▁set , ▁is ▁permitted ▁and ▁yet ▁" gu arante e " ▁acceptable ▁performance ? ▁In ▁every day ▁terms