▁at ▁a ▁general ▁level . ▁In ▁a ▁series ▁of ▁experiments , ▁training ▁with ▁interactive ▁computer ▁games ▁that ▁provided ▁players ▁with ▁personal ized ▁feedback , ▁mit ig ating ▁strateg ies , ▁and ▁practice , ▁reduced ▁six ▁cogn itive ▁bi ases ▁by ▁more ▁than ▁ 3 0 % ▁immediately ▁and ▁by ▁more ▁than ▁ 2 0 % ▁as ▁long ▁as ▁three ▁months ▁later . ▁The ▁bi ased ▁reduced ▁were ▁anch oring , ▁bias ▁blind ▁spot , ▁confirm ation ▁bias , ▁fundamental ▁att ribution ▁error , ▁projection ▁bias , ▁and ▁represent ativ eness . ▁ ▁Training ▁in ▁reference ▁class ▁forec ast ing ▁may ▁also ▁improve ▁out comes . ▁Reference ▁class ▁forec ast ing ▁is ▁a ▁method ▁for ▁system atically ▁deb ias ing ▁estimates ▁and ▁dec isions , ▁based ▁on ▁what ▁Daniel ▁K ahn eman ▁calls ▁the ▁outside ▁view . ▁As ▁pointed ▁out ▁by ▁K ahn eman ▁in ▁Th inking , ▁Fast ▁and ▁S low ▁( p . ▁ 2 5 2 ), ▁one ▁of ▁the ▁reasons ▁reference ▁class ▁forec ast ing ▁is ▁effective ▁for ▁deb ias ing ▁is ▁that , ▁in ▁contrast ▁to ▁conventional ▁forec ast ing ▁methods , ▁it ▁takes ▁into ▁account ▁the ▁so - called ▁" unknown ▁unknown s ." ▁According ▁to ▁K ahn eman , ▁reference ▁class ▁forec ast ing ▁is ▁effective ▁for ▁deb ias ing ▁and ▁" has ▁come ▁a ▁long ▁way " ▁in ▁practical ▁implementation ▁since ▁he ▁originally ▁proposed ▁the ▁idea ▁with ▁Am os ▁T ver sky ▁( p . ▁ 2 5 1 ). ▁ ▁Sometimes ▁effective ▁strateg ies ▁