▁by ▁academ ics ▁and ▁policy ▁mak ers ▁than ▁in cent ives ▁and ▁n ud ges ▁because ▁initial ▁deb ias ing ▁training ▁efforts ▁resulted ▁in ▁mixed ▁success ▁( see ▁F isch hoff , ▁ 1 9 8 2 ▁in ▁K ahn eman ▁et ▁al .). ▁Dec ision ▁mak ers ▁could ▁be ▁effectively ▁deb i ased ▁through ▁training ▁in ▁specific ▁domains . ▁For ▁example , ▁exper ts ▁can ▁be ▁trained ▁to ▁make ▁very ▁accurate ▁dec isions ▁when ▁decision ▁making ▁ent ails ▁recogn izing ▁patterns ▁and ▁applying ▁appropriate ▁responses ▁in ▁domains ▁such ▁as ▁fire f ight ing , ▁ch ess , ▁and ▁weather ▁forec ast ing . ▁Ev idence ▁of ▁more ▁general ▁deb ias ing , ▁across ▁domains ▁and ▁different ▁kinds ▁of ▁problems , ▁however , ▁was ▁not ▁discovered ▁until ▁recently . ▁The ▁reason ▁for ▁the ▁lack ▁of ▁more ▁domain - general ▁deb ias ing ▁was ▁attributed ▁to ▁exper ts ▁failing ▁to ▁recognize ▁the ▁underlying ▁" deep ▁structure " ▁of ▁problems ▁in ▁different ▁formats ▁and ▁domains . ▁We ather ▁forec aster s ▁are ▁able ▁to ▁predict ▁rain ▁with ▁high ▁accuracy , ▁for ▁example , ▁but ▁show ▁the ▁same ▁over conf idence ▁in ▁their ▁answers ▁to ▁basic ▁t riv ia ▁questions ▁as ▁other ▁people . ▁An ▁exception ▁was ▁gradu ate ▁training ▁in ▁scientific ▁fields ▁heavily ▁reli ant ▁on ▁statistics ▁such ▁as ▁psych ology . ▁ ▁Exper iments ▁by ▁More wedge ▁and ▁colle agues ▁( 2 0 1 5 ) ▁have ▁found ▁interactive ▁computer ▁games ▁and ▁instruction al ▁videos ▁can ▁result ▁in ▁long - term ▁deb ias ing