ᐱᙳᐊᖅᑎᐅᑉ ᐃᓱᒪᒋᓗᐊᖅᑕᖓ
ᐃᓱᓕᑦᑎᓂᖅ ᐊᑯᓂ ᓴᖅᑭᓯᒪᙱᓐᓂᒥᓄᑦ “ᓴᖅᑭᒋᐊᖃᓕᖅᑐᖅ” ᐃᓱᒪᒋᓂᖓ.
ᖁᓕᑦ ᐊᐅᐸᓗᒃᑐᑦ ᑭᖑᓂᖏᓐᓂ ᕿᕐᓂᖅᑐᐅᑉ ᐱᔪᓐᓇᕐᓂᖓ ᓱᓕ 18/37. ᐊᒃᓴᓗᐊᖅ ᓱᓇᒥᒃ ᐃᖃᐅᒪᙱᑦᑐᖅ ᐊᑭᓕᒋᐊᖃᙱᑦᑐᕐᓗ.
ᐃᓱᒪᒋᓗᐊᖅᑕᐅᔪᖅ
ᐊᐅᐸᓗᒃᑐᖅᐱᙳᐊᕈᑎᐊᐅᐸᓗᒃᑐᖅᐊᔾᔨᒌᒃᑐᒥᒃ ᐊᑭᓕᐅᑎᓕᒃ ᐱᙳᐊᕈᑎ 18 ᐊᐅᐸᓗᒃᑐᓄᑦ ᓈᓴᐅᑎᓄᑦ. ᕖᓇᓯᑐᑦ: ᕈᔾ (rouge).ᐃᓘᓐᓇᖓ ᐅᖃᓕᒫᕐᓗᒍ → ᖁᓕᓐᓂᒃ ᓴᖅᑭᓯᒪᒃᐸᑦ, ᐱᙳᐊᖅᑏᑦ ᐊᒥᓱᑦ ᕿᕐᓂᖅᑐᖅᐱᙳᐊᕈᑎᕿᕐᓂᖅᑐᖅᐊᔾᔨᒌᒃᑐᒥᒃ ᐊᑭᓕᐅᑎᓕᒃ ᐱᙳᐊᕈᑎ 18 ᕿᕐᓂᖅᑐᓄᑦ ᓈᓴᐅᑎᓄᑦ. ᕖᓇᓯᑐᑦ: ᓄᐊᕐ (noir).ᐃᓘᓐᓇᖓ ᐅᖃᓕᒫᕐᓗᒍ → “ᓴᖅᑭᒋᐊᖃᓕᖅᑐᖅ” ᐃᓱᒪᕙᒃᑐᑦ ᐊᒻᒪ ᑖᔅᓱᒧᖓ ᐱᙳᐊᕈᑎᓕᐅᖅᓗᑎᒃ. ᐊᓯᖏᑦ ᐊᐅᐸᓗᒃᑐᖅ “ᐆᓇᖅᑐᖅ” ᐃᓱᒪᕙᒃᑐᑦ ᐊᒻᒪ ᑖᔅᓱᒧᖓ ᐱᙳᐊᕈᑎᓕᐅᖅᓗᑎᒃ. ᑕᒪᕐᒥᒃ ᐃᓱᓕᑦᑐᒥᑦ ᑐᓪᓕᖅᑐᓂᑦ ᐱᑦᑕᓚᐅᙱᑦᑐᒧᑦ ᑲᐃᕙᕐᓂᕐᒧᑦ ᐃᓱᒪᓱᖅᑐᑦ. ᐊᖅᓴᖅᐊᑐᖅᑕᐅᔪᑦᐊᖅᓴᖅᒥᑭᔪᖅ ᐊᖅᓴᖅ ᐊᖁᑎᖓᓂ ᑲᐃᕙᖅᑐᑉ ᐊᑉᐱᖓᓄᑦ ᐃᒋᑕᐅᔪᖅ. ᒫᓐᓇᐅᔪᖅ ᐊᖅᓵᑦ ᐃᕗᕆᓐ ᐅᕝᕙᓘᓐᓃᑦ ᑎᕙᓚᓐ, 18–21 mm.ᐃᓘᓐᓇᖓ ᐅᖃᓕᒫᕐᓗᒍ → ᖁᓕᓐᓂᒃ ᑭᖑᓪᓕᖅᐹᓂᒃ ᑲᐃᕙᕐᓂᓂᒃ ᖃᐅᔨᒪᙱᑦᑐᖅ; ᐃᓂᑦᐊᑐᖅᑕᐅᔪᑦᐃᓂᑲᐃᕙᖅᑐᒥ 37 ᓈᓴᐅᑎᓕᑦ ᐃᓂᑦ ᐊᑕᐅᓯᖓ. ᐊᓯᐊᓂ ᐊᑎᖏᑦ: ᐃᓂᒋᐊᖅ, ᖃᓪᓗᐊᖅ.ᐃᓘᓐᓇᖓ ᐅᖃᓕᒫᕐᓗᒍ → ᐃᑲᕐᕋᖅ ᐊᓂᒍᖅᑐᖅ ᐊᖏᓂᖃᓚᐅᖅᑕᖓᑎᒃ ᐊᖏᓂᖃᖅᑐᑦ; ᕿᕐᓂᖅᑐᐅᑉ ᐱᔪᓐᓇᕐᓂᖓᓈᓴᐃᓂᖅᐱᔪᓐᓇᕐᓂᖅᐱᓐᓂᐅᑉ ᐱᔪᓐᓇᕐᓂᖓ, 0-ᒥᑦ 1-ᒧᑦ ᐅᕝᕙᓘᓐᓃᑦ 0%-ᒥᑦ 100%-ᒧᑦ. ᓈᓴᐅᑎ ᐊᑕᐅᓯᖅ: 1/37 = 2.70%.ᐃᓘᓐᓇᖓ ᐅᖃᓕᒫᕐᓗᒍ → 18/37, ᑕᒪᑦᑕᐃᓐᓇ ᐃᓚᐅᓚᐅᖅᑐᑎᑐᑦ.
ᓱᒻᒪᑦ ᓱᓕᔪᑎᑐᑦ ᐃᓱᒪᒋᔭᐅᕙ
ᐊᑯᓂᐅᓂᖅᓴᒥ ᐊᐅᐸᓗᒃᑐᑦ ᕿᕐᓂᖅᑐᓪᓗ ᐃᓚᖏᑦ ᐊᑕᐅᓯᑐᑦ 18/37-ᒧᑦ ᖃᓂᒡᓕᕙᒃᑐᑦ. ᐊᒥᓱᑦ ᓈᓴᐅᑏᑦ ᒪᓕᒐᖓᓈᓴᐃᓂᖅᐊᒥᓱᑦ ᓈᓴᐅᑏᑦ ᒪᓕᒐᖓᐃᒻᒥᓂᖅᑐᑦ ᐱᓐᓂᐅᔪᑦ ᐊᔾᔨᒌᒃᑎᑕᐅᔪᖓ ᓂᕆᐅᒋᔭᐅᔪᖅ ᐊᑭᖓᓄᑦ ᖃᓂᒡᓕᕙᒃᑐᖅ ᐱᓇᓱᐊᕈᑏᑦ ᐊᒥᓲᓕᖅᐸᓪᓕᐊᑎᓪᓗᒋᑦ.ᐃᓘᓐᓇᖓ ᐅᖃᓕᒫᕐᓗᒍ → ᑖᓐᓇ ᓱᓕᓪᓚᕆᒃᑎᑦᓯᔪᖅ. ᑭᓯᐊᓂ ᖃᓂᒡᓕᕐᓂᖅ ᐃᓚᒋᐊᖅᓯᓂᒃᑯᑦ ᐱᑦᑕᖅᐸᒃᑐᖅ, ᐋᖅᑭᒃᓯᓂᒃᑯᑦ ᐱᑦᑕᙱᑦᑐᖅ: 1,000 ᑲᐃᕙᕐᓃᑦ ᐃᓚᒋᐊᖅᑕᐅᔪᑦ ᑭᖑᓂᖏᓐᓂ ᖁᓕᑦ ᐃᓚᒋᐊᖅᑕᐅᔪᑦ ᐊᐅᐸᓗᒃᑐᑦ ᐃᓚᖏᓐᓂ ᒥᑭᑦᑐᐊᓘᓕᖅᑐᑦ, ᕿᕐᓂᖅᑐᖅ ᐊᑐᓪᓚᕆᐊᓕᖅᑐᓂ ᐱᓪᓗᒍ ᐱᑦᑕᙱᑦᑐᖅ, ᑕᑯᔭᐅᓯᒪᔪᑦ ᐊᒥᓲᓕᕐᒪᑕ ᑭᓯᐊᓂ. ᕿᕐᓂᖅᑐᒥᒃ ᐱᔪᓐᓇᕐᓂᖅᓴᐅᑎᑦᓯᔪᖃᙱᑦᑐᖅ, ᐊᒻᒪ ᐊᒥᓱᑦ ᓈᓴᐅᑏᑦ ᒪᓕᒐᖓ ᑕᒪᑐᒥᖓ ᐱᔭᕆᐊᖃᙱᑦᑐᖅ.
ᒪᓐᑎ ᑳᓗ, ᐊᐅᒡᒍᓯ 18, 1913
ᕿᕐᓂᖅᑐᖅ 26-ᓂᒃ ᑐᓪᓕᖅᑐᓂ ᓴᖅᑭᓯᒪᔪᖅ Casino de Monte-Carlo-ᒥ ᐱᙳᐊᕐᕕᒻᒥ. ᕿᕐᓂᖅᑐᑦ ᖁᓕᑦ ᑕᓪᓕᒪᓪᓗ ᖃᓂᒋᔮᓂ ᓴᖅᑭᓚᐅᖅᑎᓪᓗᒋᑦ, ᐱᙳᐊᖅᑏᑦ ᐊᐅᐸᓗᒃᑐᒧᑦ ᐊᒥᓱᐊᓗᐃᑦ ᐱᙳᐊᕈᑎᓕᐅᓚᐅᖅᓯᒪᔪᑦ, ᒪᕐᕉᕆᐊᖅᓗᒍ ᐊᒻᒪ ᒪᕐᕉᕆᐊᖅᑲᓐᓂᖅᓗᒍ, ᑲᐃᕙᕐᓂᖅ ᑕᒪᐃᓐᓂ ᓯᕗᓂᖓᓂᑦ “ᓱᓕᓂᖅᓴᐅᓗᓂ”. ᑳᓯᓄ ᒥᓕᐊᓐ ᐊᒥᓱᓂᒃ ᕗᕋᓐᓂᒃ ᐊᔪᒐᖅᓯᒪᔪᖅ. ᑐᓪᓕᖅᑐᑦ ᐱᔪᓐᓇᕐᓂᖏᑦ 1 137 ᒥᓕᐊᓂᑦ ᓯᕗᓪᓕᖅᐹᒥᑦ ᕿᕐᓂᖅᑐᒥᑦ, ᐊᒻᒪ ᓯᓚᕐᔪᐊᕐᒥ ᑳᓯᓄᓂ ᓇᓃᑦᑐᖅ ᐊᑕᐅᑦᑎᒃᑯᑦ ᐱᑦᑕᕐᓂᐊᕐᓂᖓᓄᑦ ᐱᔪᓐᓇᕐᓂᖅ ᐊᑕᐅᓯᖅ ᖃᓂᒋᔮᓂ. ᑖᓐᓇ ᐱᓪᓗᒍ ᐱᙳᐊᖅᑎᐅᑉ ᐃᓱᒪᒋᓗᐊᖅᑕᖓ ᒪᓐᑎ ᑳᓗᐅᑉ ᐃᓱᒪᒋᓗᐊᖅᑕᖓᓂᒃ ᑕᐃᔭᐅᕙᒃᑐᖅ.
ᑐᐊᕕᕐᓇᙱᑦᑐᖅ. ᐊᑕᐅᑦᑎᒃᑯᑦ 44-ᓂᑦ ᐱᑦᑕᖅᐸᒃᑐᖅ.
ᐱᙳᐊᖅᑏᑦ ᖃᐅᔨᓕᖅᑐᑦ. ᐊᐅᐸᓗᒃᑐᖅ “ᓴᖅᑭᒋᐊᖃᓕᖅᑐᖅ”.
ᐱᙳᐊᕐᕕᒃ ᐃᓄᒋᐊᒃᑐᖅ. ᐊᐅᐸᓗᒃᑐᒧᑦ ᐱᙳᐊᕈᑎ ᑮᓇᐅᔭᖏᑦ ᐊᓯᐅᔾᔨᓂᖅ ᑕᒪᐃᓐᓂ ᑭᖑᓂᖓᓂ ᒪᕐᕉᕆᐊᖅᑕᐅᕙᒃᑐᑦ.
ᐊᒥᓱᐊᓗᐃᑦ ᑮᓇᐅᔭᑦ ᐊᓯᐅᔾᔨᑕᐅᔪᑦ. ᕿᕐᓂᖅᑐᖅ ᓄᑖᖅ ᑕᒪᐃᓐᓂ ᐊᐅᐸᓗᒃᑐᒥᒃ ᓱᓕᓂᖅᓴᐅᓕᖅᑐᑎᑐᑦ ᐃᓱᒪᒋᔭᐅᑎᑦᓯᔪᖅ.
ᑐᓪᓕᖅᑐᑦ ᐃᓱᓕᑦᑐᑦ. ᑳᓯᓄ ᒥᓕᐊᓐ ᐊᒥᓱᓂᒃ ᕗᕋᓐᓂᒃ ᐊᔪᒐᖅᓯᒪᔪᖅ ᐃᓄᓐᓂᑦ ᑐᓪᓕᖅᑐᓄᑦ ᐊᑐᓪᓚᕆᐊᖅᓗᑎᒃ ᐱᙳᐊᕈᑎᓕᐅᖅᑐᓂᑦ, ᑖᒃᑯᐊ ᑐᓪᓕᖅᑐᐅᓂᖏᓐᓂᒃ “ᖃᐅᔨᒪᔪᓐᓇᙱᑦᑐᑦ”.
ᐆᓇᖅᑐᖅ ᐊᒡᒐ
ᑕᕐᕋᖓᓄᑦ ᑐᓪᓕᖅᑐᓂ ᐃᓚᐅᓂᖓᓄᑦ ᐱᙳᐊᕈᑎᓕᐅᕐᓂᖅ ᐊᔾᔨᒌᒃᑐᖅ ᑕᒻᒪᕐᓂᖅ ᐅᑎᖅᑕᖅ: ᑐᓪᓕᖅᑐᑦ ᐃᓱᓕᓐᓂᐊᓐᖏᑦᑐᑦ ᑲᔪᓯᓂᐊᖅᑐᑎᑐᑦ ᓂᕆᐅᒋᔭᐅᔪᑦ. ᐃᓱᒪᓕᕆᔩᑦ ᐆᓇᖅᑐᒥᒃ ᐊᒡᒐᒥᒃᓱᓕᙱᑦᑐᖅᐆᓇᖅᑐᖅ ᐊᒡᒐᐅᐱᒪᓗᐊᕐᓂᖅ ᐊᔪᒐᕐᓃᑦ ᑐᓪᓕᖅᑐᑦ, ᐅᕝᕙᓘᓐᓃᑦ ᐱᓐᓂᖅ ᐊᑕᐅᓯᖅ ᑐᓪᓕᖅᑐᖅ, ᐊᓯᖓ ᐱᔪᓐᓇᕐᓂᖅᓴᐅᑎᑦᓯᓂᕋᖅᓗᒍ. ᐊᒃᓴᓗᐊᕐᒥ ᐱᙳᐊᖅᑎᐅᑉ ᐃᓱᒪᒋᓗᐊᖅᑕᖓ ᐅᑎᖅᑕᖅ.ᐃᓘᓐᓇᖓ ᐅᖃᓕᒫᕐᓗᒍ → ᑕᐃᓯᕙᒃᑐᑦ, 1985-ᒥ ᒋᓗᕕᑦᓯ (Gilovich), ᕙᓗᓐ (Vallone) ᑐᕗᐊᔅᑭᓗ (Tversky) ᖃᐅᔨᓴᕐᓂᖓᑕ ᑭᖑᓂᖓᓂ, ᐹᔅᑭᑦᐹᓪᒥ ᒥᕐᓂᓂ ᑐᓪᓕᖅᑐᓂᒃ ᕿᓂᖅᓗᑎᒃ ᐱᔪᓐᓇᕐᓂᒃᑯᑦ ᐱᑦᑕᕐᓂᐅᑉ ᓯᓚᑖᓂ ᓇᓂᓯᓯᒪᙱᑦᑐᑦ. 2018-ᒥ ᒥᓚ (Miller) ᓴᓐᕼᐅᕐᕼᐅᓗ (Sanjurjo) ᓇᓗᓇᐃᖅᓯᓯᒪᔪᒃ ᖃᐅᔨᓴᕈᑎ ᓇᒻᒥᓂᖅ ᓴᖑᓯᒪᓚᐅᖅᑐᖅ: ᑎᑎᕋᖅᓯᒪᔪᒥ ᓇᐃᑦᑐᒥ, ᐊᔪᒐᕐᓂᐅᑉ ᑭᖑᓂᖓᓂ ᐊᔪᒐᕐᓃᑦ ᐃᓚᖏᑦ ᐊᔾᔨᒌᒃᑎᓪᓗᒋᑦ ᒥᕐᓂᖅᑎᐅᑉ ᑕᒪᐃᓐᓂ ᐃᓚᖓᓂᑦ ᒥᑭᓂᖅᓴᐅᕙᒃᑐᑦ, ᑕᐃᒪ ᐱᙳᐊᖅᑎ ᐆᓇᖅᑐᒥᒃ ᐊᒡᒐᖃᙱᑦᑐᐊᓗᒃ ᐃᒃᑮᔪᓗᒃᑐᑎᑐᑦ ᑕᑯᓐᓇᖅᑐᖅ. ᐋᖅᑭᒃᑕᐅᓪᓗᑎᒃ, ᐊᔾᔨᒌᒃᑐᑦ ᓈᓴᐅᑏᑦ ᒥᑭᔪᒥᒃ ᐆᓇᖅᑐᒥᒃ ᐊᒡᒐᒥᒃ ᓇᓗᓇᐃᖅᓯᔪᑦ. ᐃᓄᒃ ᐆᓇᓕᕈᓐᓇᖅᑐᖅ; ᐊᒃᓴᓗᐊᖅ ᐱᐅᓯᖃᙱᑦᑐᖅ. ᐱᙳᐊᖅᑏᑦ ᐃᓚᖏᑦ ᒪᕐᕉᓂᒃ ᐅᐱᒍᓱᒃᑐᑦ ᐊᑕᐅᑦᑎᒃᑯᑦ, ᑐᓪᓕᖅᑐᑦ “ᐊᑯᓂᐅᓗᐊᓕᖅᑎᓐᓇᒋᑦ” ᑲᔪᓯᓂᖏᓐᓂᒃ ᑕᐃᒪ ᐃᓱᓕᓐᓂᖏᓐᓂᒃ. ᓈᒻᒪᒃᑐᒥ ᐊᒃᓴᓗᐊᕐᒥ ᑕᒪᒃᑯᐊ ᑕᒪᕐᒥᒃ ᐊᔾᔨᒌᒃᑐᒥᒃ ᓂᕆᐅᒋᔭᐅᔪᒥᒃ ᐊᑭᖃᖅᑐᑦᓈᓴᐃᓂᖅᓂᕆᐅᒋᔭᐅᔪᖅ ᐊᑭᖓᐱᙳᐊᕈᑎ ᐊᑐᓂ ᐊᑯᓂᐅᓂᖅᓴᒥ ᐊᔾᔨᒌᒃᑎᑕᐅᔪᖅ ᐱᓐᓂᖓ. ᐃᐅᕌᐸ ᐱᙳᐊᕈᑎ ᑕᒪᐃᓐᓂ −1/37 ᐱᙳᐊᕈᑎ ᑮᓇᐅᔭᖓᓂᑦ.ᐃᓘᓐᓇᖓ ᐅᖃᓕᒫᕐᓗᒍ → ᑕᕐᕋᖓᓂᒃ ᑮᓇᐅᔭᕐᒥᒃ ᒥᓯᒃᓯᓂᒃᑯᑦ ᓇᓚᐅᑦᑖᕐᓂᕐᒥᒃ: −2.70% ᐱᙳᐊᕈᑎ ᑮᓇᐅᔭᖓᓂᒃ.
ᓱᓕᔪᒥ ᐱᙳᐊᕐᕕᒻᒥ
ᑯᕉᓴᓐ (Croson) ᓴᓐᑕᓕᓗ (Sundali) (2005) 18 ᐃᑲᕐᕋᓂᒃ ᓴᐳᔾᔨᓂᕐᒧᑦ ᑕᕐᕆᔭᒐᓂᒃ ᑕᑯᓯᒪᔪᒃ ᒪᕐᕉᒃ ᓯᕉᓕᒻᒥᐊᒃᓴᓗᐊᖅᓯᕉᐊᑕᐅᓯᖅ ᐅᔾᔪᐊᕌᔭᒃ ᐃᓂ ᐊᔾᔨᒌᒃᑐᒥᒃ ᐊᑭᓕᐅᑎᓕᖕᓄᑦ, ᑕᓴᓐᓄᑦ ᓇᑉᐸᖅᑐᓄᓪᓘᓐᓃᑦ ᐱᙳᐊᕈᑏᑦ ᐃᓚᐅᑎᙱᑕᖓ; ᑳᓯᓅᑉ ᐃᓚᖓᑕ ᐱᓇᓪᓗᑎᖓ. ᐊᓯᐊᓂ ᐊᑎᖏᑦ: ᐊᑕᐅᓯᖅ ᓯᕉ, 0.ᐃᓘᓐᓇᖓ ᐅᖃᓕᒫᕐᓗᒍ → ᐱᙳᐊᕐᕕᒻᒥ ᐊᑕᐅᓯᕐᒥ ᑳᓯᓄᒥ ᐊᖏᔪᒥ ᕇᓄᒥ: 139 ᐱᙳᐊᖅᑏᑦ 24,131 ᐱᙳᐊᕈᑎᓂᒃ ᐃᓕᓯᔪᑦ. ᐊᔾᔨᒌᒃᑐᒥᒃ ᐊᑭᓕᐅᑎᓕᓐᓂ ᐱᙳᐊᕈᑎᓂᐱᙳᐊᕈᑎᐊᔾᔨᒌᒃᑐᒥᒃ ᐊᑭᓕᐅᑎᓕᒃ ᐱᙳᐊᕈᑎᐊᐅᐸᓗᒃᑐᖅ/ᕿᕐᓂᖅᑐᖅ, ᐊᕕᒃᑕᐅᔪᓐᓇᙱᑦᑐᖅ/ᐊᕕᒃᑕᐅᔪᓐᓇᖅᑐᖅ, ᐊᖏᔪᑦ/ᒥᑭᔪᑦ. 18 ᓈᓴᐅᑏᑦ, ᐊᑭᓕᐅᑎ 1:1.ᐃᓘᓐᓇᖓ ᐅᖃᓕᒫᕐᓗᒍ →, ᐱᙳᐊᖅᑏᑦ ᓇᑉᐸᖓ ᖃᓂᒋᔮᓂ ᓇᐃᑦᑐᓂᒃ ᑐᓪᓕᖅᑐᓂᒃ ᒪᓕᒃᓯᔪᑦ ᓇᑉᐸᖓᓗ ᐊᑐᓪᓚᕆᐊᖅᓗᑎᒃ. ᑕᓪᓕᒪᑦ ᐊᔾᔨᒌᒃᑐᑦ ᑐᓪᓕᖅᑐᑦ ᑭᖑᓂᖏᓐᓂ ᑐᓪᓕᖅᑐᓄᑦ ᐊᑐᓪᓚᕆᐊᓂᖅᓴᐅᓕᖅᑐᑦ, ᐱᖓᓲᔪᖅᑐᑦ ᐅᕝᕙᓘᓐᓃᑦ ᐅᖓᑖᓄᑦ ᑭᖑᓂᖏᓐᓂᓗ ᐱᙳᐊᕈᑎᖏᑦ 85%-ᖏᑦ ᐊᓯᐊᓄᑦ ᐃᓕᓯᒪᔪᑦ. ᐊᔾᔨᒌᒃᑐᑦ ᐱᙳᐊᖅᑏᑦ ᐆᓇᖅᑐᒥᒃ ᐊᒡᒐᒥᒃ ᐃᒻᒥᓂᒃ ᐅᐱᒍᓱᒃᑐᑦ: ᓈᓴᐅᑎᓂ ᐊᔪᒐᕐᓂᖅ ᑭᖑᓂᖓᓂ ᑐᓪᓕᐊᓂ ᑲᐃᕙᕐᓂᕐᒥ ᐊᔾᔨᒌᒃᑎᓪᓗᒋᑦ 13.6 ᓈᓴᐅᑎᓂᒃ ᐱᙳᐊᕈᑎᒋᔪᑦ, 9.2-ᖑᓪᓗᓂ ᐊᓯᐅᔾᔨᓂᖅ ᑭᖑᓂᖓᓂ. ᐊᒃᓴᓗᐊᖓᑦ ᓴᖑᓯᒪᓂᕐᒥᒃ ᓇᓗᓇᐃᖅᓯᓯᒪᙱᑦᑐᖅ, ᑕᐃᒪ ᑕᒪᒃᑯᐊ ᑕᒪᕐᒥᒃ ᓇᓕᐊᑕ ᓂᕆᐅᒋᔭᖓᓂᒃ ᐊᓯᔾᔨᖅᑎᑦᓯᓯᒪᙱᑦᑐᑦ.
ᑳᓯᓄᐅᑉ ᓯᓚᑖᓂ
ᐊᔾᔨᒌᒃᑐᖅ ᐃᓱᒪᒋᓗᐊᕐᓂᖅ ᓱᓕᔪᓂᒃ ᐃᓱᒪᓕᐅᕐᓂᓂ ᓴᖅᑭᕙᒃᑐᖅ. ᑲᓗᑦᕙᓪᑐ (Clotfelter) ᑰᒡᓗ (Cook) (1993) ᒥᐊᕆᓚᓐᑦᒥ ᐅᓪᓗᑕᒫᖅ ᓈᓴᐅᑏᑦ ᐱᙳᐊᕐᓂᖓᓂᒃ ᒪᓕᒃᓯᓯᒪᔪᒃ: ᓈᓴᐅᑎᒧᑦ ᐱᙳᐊᕈᑎᒋᔭᐅᔪᑦ ᑮᓇᐅᔭᑦ ᐊᒡᒍᐊᓗᒃ ᒥᑭᓕᓚᐅᖅᓯᒪᔪᑦ ᐱᔭᐅᓚᐅᖅᑎᓪᓗᒍ ᐊᒻᒪ ᑕᖅᑭᓂ ᐊᒥᓱᓂ ᐅᑎᖅᑕᐅᓪᓗᑎᒃ, ᐱᔭᐅᓂᖓ ᐃᖃᐅᒪᔾᔪᑎᖃᙱᒃᑲᓗᐊᖅᑎᓪᓗᒍ. ᓯᐊᓐ (Chen), ᒨᔅᑲᕕᑦᔅ (Moskowitz) ᓲᓗ (Shue) (2016) ᐱᓕᕆᐊᓕᓐᓂ ᓇᓂᓯᓯᒪᔪᑦ. ᐊᒥᐊᓕᑲᒥ ᓄᓇᖃᕈᒪᔪᓄᑦ ᐃᖅᑲᖅᑐᐃᔩᑦ 3.3 ᐃᓚᖏᓐᓂᒃ ᐊᑕᐅᓯᕐᒥᒃ ᐊᓪᓚᒃᑲᓐᓂᕐᓂᑦ ᐊᖏᔪᒥᒃ ᐱᓗᐊᕐᓂᖅᓴᐅᓯᒪᔪᑦ ᐃᖅᑲᖅᑐᐃᓂᕐᒥᒃ ᐊᖏᙱᓐᓂᕐᒧᑦ ᓯᕗᓂᐊᓂ ᐊᖏᓯᒪᒃᐸᑕ. ᐸᐃᔅᐹᓪᒥ ᐃᖅᑲᖅᑐᐃᔩᑦ 1.5 ᐃᓚᖏᓐᓂᒃ ᓯᑐᕌᐃᒃ ᐅᖃᐅᓯᐅᓂᖓᓄᑦ ᒥᑭᓂᖅᓴᐅᓯᒪᔪᑦ ᐊᑕᐅᓯᕐᒥᒃ ᐅᖃᐅᓯᖃᓚᐅᖅᑎᓪᓗᒋᑦ, ᐊᒻᒪ ᒪᕐᕉᕆᐊᖅᓗᒍ ᐅᖓᑖᓄᑦ ᖃᓂᒋᔮᖅᑐᓂ ᒥᕐᓂᓂ. ᐅᓯᐊᖅᑕᐅᓂᕐᒥ ᐊᑐᐃᓐᓇᕈᖅᑎᑕᐅᔪᓄᑦ ᑮᓇᐅᔭᓕᕆᔩᑦ ᐊᔾᔨᒌᒃᑐᒥᒃ ᐊᑭᓕᖅᑕᐅᔪᑦ, 9% ᑎᑭᓪᓗᒍ ᐃᓱᒪᓕᐅᕐᓃᑦ ᐅᑎᖅᑕᖅᑐᑦ ᑕᒪᓐᓇ ᐱᐅᓯᖓᓂ. ᐊᐱᖅᑯᑏᑦ ᐱᔪᓐᓇᕐᓂᒃᑯᑦ ᑐᓪᓕᑎᒌᓐᓂᖏᑎᒍᑦ ᑎᑭᑦᑐᑦ, ᑕᐃᒪ ᑭᖑᓪᓕᖅᐹᖅ ᐃᓱᒪᓕᐅᕐᓂᖅ ᑐᓪᓕᐊᓂᒃ ᓱᓇᒥᒃ ᐅᖃᐅᓯᖃᙱᑦᑐᖅ, ᑭᖑᓪᓕᖅᐹᖅ ᑲᐃᕙᕐᓂᖅ ᑐᓪᓕᐊᓂᒃ ᓱᓇᒥᒃ ᐅᖃᐅᓯᖃᙱᑦᑐᑎᑐᑦ.
ᐱᙳᐊᖅᑎᐅᑉ ᐃᓱᒪᒋᓗᐊᖅᑕᖓ ᐅᑎᖅᑕᖅ
ᐃᓱᒪᓱᖅᑎ ᐄᐊᓐ ᕼᐋᑭᖕ (Ian Hacking) (1987) ᐱᖓᔪᐊᓂᒃ ᐱᐅᓯᕐᒥᒃ ᑕᐃᓯᓯᒪᔪᖅ. ᐃᓄᒃ ᐃᒡᓗᒧᑦ ᐃᓯᖅᑐᖅ, ᐱᙳᐊᕈᓯᐊᒃ ᒪᕐᕉᒃ ᒪᕐᕉᓐᓂᒃ ᐱᖓᓲᔪᖅᑐᓂᒃ ᑐᒃᑐᖅᑐᒃ ᑕᑯᓪᓗᒋᒃ ᐊᒻᒪ ᐊᒥᓱᐊᖅᑐᓂ ᐃᒥᖅᑕᐅᓯᒪᓂᕋᐃᓪᓗᒋᒃ. ᐃᒥᖅᑕᐅᓂᖅ ᑕᒪᐃᓐᓂ ᐊᔾᔨᒌᒃᑐᒥᒃ ᐱᔪᓐᓇᕐᓂᖃᖅᑐᖅ ᒪᕐᕉᓐᓂᒃ ᐱᖓᓲᔪᖅᑐᓂᒃ, ᐅᓐᓄᐊᑉ ᓯᕗᓪᓕᖅᐹᖓ ᐅᕝᕙᓘᓐᓃᑦ ᑕᐅᓴᓐᖓ, ᑕᐃᒪ ᑕᑯᔭᐅᔪᖅ ᐱᙳᐊᕐᓂᖅ ᖃᓄᖅ ᐊᑯᓂ ᐱᒋᐊᓚᐅᖅᓯᒪᓂᖓᓂᒃ ᓱᓇᒥᒃ ᐅᖃᐅᓯᖃᙱᑦᑐᖅ. ᕉᓚᑦᒥ ᐱᙳᐊᖅᑎ ᑎᑭᑦᑐᖅ ᐊᔪᒐᐅᑎᑦ ᓇᓗᓇᐃᖅᑯᑎᖏᓐᓄᑦ ᐊᕐᕕᓂᓖᑦ ᒪᕐᕉᓐᓂᒃ ᐊᐅᐸᓗᒃᑐᓂᒃ ᓇᓗᓇᐃᖅᓯᔪᓄᑦ ᐊᒻᒪ ᐱᙳᐊᕐᕕᒃ ᐃᑲᕐᕋᓂ ᐊᒥᓱᓂ ᑲᐃᕙᑦᑎᑕᐅᓯᒪᓂᕋᐃᓪᓗᒍ ᑖᒃᑯᓂᖓ ᓴᖅᑭᑎᑦᓯᓂᕐᒧᑦ. ᐊᑐᕐᓗᐊᖑᔪᖅ ᐃᓱᒪᒋᓗᐊᕐᓂᖅ ᐊᓂᒍᖅᓯᒪᔪᓂᒃ ᑐᓪᓕᐊ ᑲᐃᕙᕐᓂᕐᒧᑦ ᐃᓱᒪᒋᔪᖅ; ᑖᓐᓇ ᐱᑦᑕᕐᕕᐅᓐᖏᑦᑐᒥᒃ ᑕᑯᔭᐅᔪᒥᒃ ᐊᓂᒍᖅᓯᒪᔪᓄᑦ ᑕᑯᔭᐅᓚᐅᙱᑦᑐᓄᑦ ᐃᓱᒪᒋᔪᖅ.
ᐃᓱᒪᓕᕆᔨ ᐅᐱᒍᓱᒃᑐᖅ
ᐃᓱᒪᒋᓗᐊᖅᑕᐅᔪᖅ ᐃᓕᓐᓂᐊᓯᒪᔪᓂᑦ ᐱᔾᔪᑎᒋᔭᐅᓯᒪᔪᖅ. ᔮᒪᓂ ᐃᓱᒪᓕᕆᔨ ᑳᓪ ᒫᐱ (Karl Marbe) ᓯᑕᒪᓂᒃ ᐃᓅᓪᓗᐊᑦᑕᕐᓂᕐᒥᒃ ᑎᑎᕋᖅᓯᒪᔪᓂᒃ 49,152-ᓂᒃ ᐊᑕᐅᓯᑐᑦ ᐱᑕᖃᖅᑐᓂᒃ ᖃᐅᔨᓴᖅᓯᒪᔪᖅ, ᐊᖑᑎᑦ ᐅᕝᕙᓘᓐᓃᑦ ᐊᕐᓇᐃᑦ ᑐᓪᓕᖅᑐᑦ ᐊᑯᓂᓛᖑᔪᑦ 17-ᖑᓂᖏᓐᓂᒃ ᓇᓂᓯᓪᓗᓂ, ᐊᒻᒪ ᐊᑯᓂᐅᔪᑦ ᑐᓪᓕᖅᑐᑦ ᐱᔪᓐᓇᕐᓂᒃᑯᑦ ᐱᑦᑕᕐᕕᐅᓐᖏᓂᖅᓴᐅᓂᕋᐃᓪᓗᒋᑦ, ᑕᐃᒪ ᑐᓪᓕᖅᑐᑦ ᐊᓯᐊᓂᒃ ᐱᔪᓐᓇᕐᓂᖅᓴᐅᑎᑦᓯᓂᕋᐃᓪᓗᒋᑦ. ᐱᔪᓐᓇᕐᓂᖅ ᐊᓯᐊᓂᒃ ᐅᖃᖅᑐᖅ: 196,608 ᐃᓅᓪᓗᐊᑦᑕᕐᓂᓂ 17 ᐅᕝᕙᓘᓐᓃᑦ ᐅᖓᑖᓄᑦ ᑐᓪᓕᖅᑐᑦ ᓯᑕᒪᓂ ᑕᓪᓕᒪᓂᑦ ᖃᓂᒋᔮᓂ ᓴᖅᑭᕙᒃᑐᑦ. ᒫᐱ ᐱᔪᓐᓇᕐᓂᒃᑯᑦ ᑐᓪᓕᑎᒌᑦᑐᑦ ᐊᔾᔨᖏᓐᓂᒃ ᓇᓂᓯᓯᒪᔪᖅ. ᐃᑉᔅᑕᐃᓐ (Epstein) (2013) ᐅᓂᒃᑳᖅᑲᓐᓂᖅᑐᖅ; ᕉᓚᑦᒥ ᑐᓪᓕᖅᑐᑦ ᐱᔪᓐᓇᕐᓂᖏᑦ ᕉᓚᑦᒥ ᐱᐅᓯᖓᓂᒃ ᓇᓗᓇᐃᖅᓯᔪᖅ.
ᐃᓱᒪᒋᓗᐊᖅᑕᐅᔪᖅ ᖃᖓ ᐊᑐᖅᑕᐅᙱᑦᑐᖅ
ᐊᒃᓴᓗᐊᖅ ᐊᐅᓚᓂᖓᒍᑦ ᓴᖑᓯᒪᒍᓂ, ᐊᓂᒍᖅᓯᒪᔪᑦ ᑲᐃᕙᕐᓃᑦ ᑐᓴᒐᒃᓴᖃᕐᓂᐊᖅᑐᑦ, ᐃᓂᓂᒃ ᓇᓗᓇᐃᖅᓯᓂᐊᕐᒪᑕ, ᐱᔪᓐᓇᕐᓂᕐᒥᒃ ᓇᓗᓇᐃᖅᓯᓐᖏᓪᓗᑎᒃ. ᑖᓐᓇ ᖃᐅᔨᓴᕐᕕᐅᑉ ᐅᖃᐅᓯᕆᔭᖓ, ᐊᒻᒪ ᑕᐅᓴᓐ ᐊᒥᓱᓂᒃ ᑲᐃᕙᕐᓂᓂᒃ ᐊᒃᓴᓗᐊᒥᑦ ᐊᑕᐅᓯᕐᒥᑦ ᓇᓗᓇᐃᖅᓯᒪᔪᒥᑦ ᐱᔭᕆᐊᖃᖅᑐᖅ ᓱᓇᒥᒃ ᐅᖃᕈᓐᓇᕐᓂᕐᒧᑦ. ᖁᓕᑦ ᐊᐅᐸᓗᒃᑐᑦ ᑐᓪᓕᖅᑐᑦ ᖃᐅᔨᒪᔭᐅᙱᑦᑐᒥ ᐊᒃᓴᓗᐊᕐᒥ, ᓴᖑᓯᒪᓂᖅ ᐱᔪᓐᓇᕐᓂᒃᑯᑦ ᐱᑦᑕᕐᓂᕐᒥᑦ ᐱᔪᓐᓇᕐᓂᐅᓐᖏᓂᖅᓴᐅᔪᐊᓘᔪᖅ.
ᐊᐱᖅᑯᑏᑦ
ᐋᒡᒐ. ᐃᒻᒥᓂᖅ ᐱᓂᕐᓗᒃᑐᓄᑦ ᐅᑎᖅᐸᒃᑐᓄᑦ ᐱᑦᑕᕐᓂᓄᑦ ᑕᒪᐃᓐᓄᑦ ᐊᑐᖅᑐᖅ: ᐱᙳᐊᕈᓯᐊᑦ, ᑮᓇᐅᔭᐃᑦ ᒥᓯᒃᓯᓂᖅ, ᓛᑕᕆ ᓈᓴᐅᑏᑦ.
ᐃᓚᖏᑦ ᖃᓂᒡᓕᕐᓂᐊᖅᑐᑦ. ᐃᓱᓕᑦᑎᓂᕐᓂ ᓱᓕ −2.70% ᖃᓂᒋᔮᓂ ᐱᙳᐊᕈᑎᒋᔭᕐᓂᑦ ᐊᓯᐅᔾᔨᓂᐅᓂᐊᖅᑐᖅ.
ᐊᔾᔨᒌᒃᑎᑕᐅᔪᑦ ᒪᓕᒐᖓ ᐱᑕᖃᙱᑦᑐᖅ. ᐊᒥᓱᑦ ᓈᓴᐅᑏᑦ ᒪᓕᒐᖓ ᐱᑕᖃᖅᑐᖅ, ᐊᒻᒪ ᐃᓚᒋᐊᖅᓯᓂᒃᑯᑦ ᐊᑐᖅᑐᖅ.
ᓈᓴᐃᓂᕐᒥᒃ ᐱᙳᐊᕐᓂᖅ
ᓯᕗᓪᓕᐅᓪᓗᓂ ᐃᓕᖕᓂᒃ ᐆᒃᑐᕐᓗᑎᑦ. ᑭᐅᔾᔪᑏᑦ ᐃᓗᓕᖏᑦ ᒪᑐᔭᐅᓯᒪᔪᑦ; ᐅᐸᒍᓐᓇᕈᕕᑦ ᒪᑐᐃᖅᓯᓗᒍ.
ᑳᐃᓐ 10-ᕆᐊᖅᓗᒍ ᐃᒋᑕᐅᒃᑲᓐᓂᓚᐅᖅᑎᓪᓗᒍ 7 ᓂᐊᖁᖓᓂᒃ 3-ᓂᒡᓗ ᑐᓄᖓᓂᒃ ᑕᑯᑎᑦᓯᔪᖅ. 1,000-ᕆᐊᖅᓗᒍ ᐃᒋᑕᐅᓚᐅᖅᑎᓪᓗᒍ 517 ᓂᐊᖁᖓᓂᒃ 483-ᓂᒡᓗ ᑐᓄᖓᓂᒃ ᑕᑯᑎᑦᓯᔪᖅ. ᑐᓄᖏᑦ ᐊᔾᔨᒌᒃᓯᓯᒪᕙᑦ?
ᑭᐅᔾᔪᑎ ᑕᑯᓗᒍ
ᑭᐅᔾᔪᑎ ᐋᒃᑲ. ᐊᔾᔨᒌᙱᓐᓂᖏᑦ 4-ᒥᑦ 34-ᒧᑦ ᐊᖏᓕᒋᐊᖅᑐᑦ. ᐊᔾᔨᒌᙱᓐᓂᐅᑉ ᐊᑦᔨᐅᑎᖓ ᑭᓯᐊᓂ ᐊᔾᔨᒌᓕᕐᓂᕐᒧᑦ ᖃᓂᓕᒋᐊᖅᑐᖅ.
ᓂᐊᖁᖏᑦ ᑐᓄᖏᓐᓄᑦ ᐊᑦᔨᐅᑎᖏᑦ 7/3-ᒥᑦ, 2.33 ᖃᓂᒋᔮᓂ, 517/483-ᒧᑦ, 1.07 ᖃᓂᒋᔮᓂ, ᒥᑭᓕᒋᐊᖅᑐᑦ. ᑕᒪᓐᓇ ᐊᒥᓱᑦ ᓈᓴᐅᑏᑦ ᒪᓕᒐᖓᓈᓴᐃᓂᖅᐊᒥᓱᑦ ᓈᓴᐅᑏᑦ ᒪᓕᒐᖓᐃᒻᒥᓂᖅᑐᑦ ᐱᓐᓂᐅᔪᑦ ᐊᔾᔨᒌᒃᑎᑕᐅᔪᖓ ᓂᕆᐅᒋᔭᐅᔪᖅ ᐊᑭᖓᓄᑦ ᖃᓂᒡᓕᕙᒃᑐᖅ ᐱᓇᓱᐊᕈᑏᑦ ᐊᒥᓲᓕᖅᐸᓪᓕᐊᑎᓪᓗᒋᑦ.ᐃᓘᓐᓇᖓ ᐅᖃᓕᒫᕐᓗᒍ → ᐊᐅᓚᔪᖅ.
ᐊᔾᔨᒌᙱᓐᓂᖏᑦ ᐊᖏᓕᒋᐊᖅᑐᑦ, 4-ᒥᑦ 34-ᒧᑦ. ᓈᒻᒪᒃᑐᒥ ᑳᐃᓐᒥ ᐊᔾᔨᒌᙱᓐᓂᖅ 30 ᖃᓂᒋᔮᓂ 1,000 ᐃᒋᑕᐅᓃᑦ ᑭᖑᓂᐊᒍᑦ ᓇᓕᓐᓇᐅᙱᑦᑐᖅ: ᓇᓕᓐᓇᐅᙱᑦᑐᖅ ᐊᔾᔨᒌᙱᓐᓂᖅ ᐃᒋᑕᐅᓃᑦ ᖃᑦᓯᐅᓂᖏᑕ ᓈᓴᐅᑎ ᐊᐱᖓᓂᒃ ᒪᓕᒃᓱᓂ ᐊᖏᓕᒋᐊᖅᐸᒃᑐᖅ, ᐊᑦᔨᐅᑎᖓᓕ 1-ᒧᑦ ᓄᖃᖅᐸᓪᓕᐊᔪᖅ.
ᓱᓇᑐᐃᓐᓇᖅ ᑐᓄᖏᓐᓂᒃ ᐅᑎᖅᑎᑦᓯᙱᑦᑐᖅ. ᓯᕗᓪᓕᐅᔪᑦ ᓂᐊᖁᖏᑦ ᐅᖓᑖᓄᑦ ᐃᓚᖏᑦ ᑐᓪᓕᐊᓂ 990 ᐃᒋᑕᐅᓃᓂ ᐃᑦᑕᕐᓂᓕᒋᐊᖅᑕᐅᔪᑦ, ᐋᖅᑭᒃᑕᐅᙱᓪᓗᑎᒃ.
ᑳᐃᓐ ᓯᑕᒪᕆᐊᖅᓗᒍ ᐃᒋᓗᒍ ᑕᑯᓗᒋᑦᓗ ᐃᒋᑕᐅᓃᑦ ᓂᐊᖁᖓ ᑐᓪᓕᐊᓂ ᑭᓯᐊᓂ. ᑕᒪᒃᑯᓇᙵᑦ ᖃᓄᑎᒋ ᐃᓚᖓ ᓂᐊᖁᖏᑦ? 16 ᐃᓱᓕᑦᑎᓂᐅᔪᓐᓇᖅᑐᓂ ᑐᓪᓕᖅᑲᑕᐅᔪᓂ ᑕᒪᒃᑭᓐᓂ ᓈᓴᐃᓗᑎᑦ ᐃᓚᖏᓪᓗ ᐊᔾᔨᒌᒃᑎᓗᒋᑦ. 50%-ᐅᕙ?
ᑭᐅᔾᔪᑎ ᑕᑯᓗᒍ
ᑭᐅᔾᔪᑎ ᐋᒃᑲ. 17/42-ᐅᔪᖅ, 40.5% ᖃᓂᒋᔮᓂ. ᑕᒪᑐᒥᖓ ᑕᑯᓐᓇᕈᑉᐱᑦ, ᓈᒻᒪᒃᑐᖅ ᑳᐃᓐ ᓂᐊᖁᖓ ᑭᖑᓂᐊᒍᑦ ᐃᒃᑮᓕᖅᑑᔮᖅᑐᖅ.
16 ᑐᓪᓕᖅᑲᑕᐅᔪᑦ ᑎᑎᕋᕐᓗᒋᑦ (H = ᓂᐊᖁᖓ, T = ᑐᓄᖓ). ᒪᕐᕉᒃ, TTTT ᐊᒻᒪ TTTH, ᓯᕗᓪᓕᖅᐹᓂ ᐱᖓᓱᓂ ᐃᒋᑕᐅᓃᓂ ᓂᐊᖁᖓᓂᒃ ᐱᖃᙱᑦᑐᒃ, ᑕᐃᒪ ᓂᐊᖁᖓ ᑐᓪᓕᐊᓂ ᓱᓇᖃᙱᑦᑐᖅ; 14 ᐱᑕᖃᖅᑐᑦ.
ᑕᒪᒃᑭᓐᓂ, ᐃᒋᑕᐅᓃᑦ ᓂᐊᖁᖓ ᑐᓪᓕᐊᓂ ᑎᒍᓪᓗᒋᑦ ᓂᐊᖁᖏᓪᓗ ᐃᓚᖏᑦ ᓇᓂᓯᓗᒋᑦ. HHHH 3-ᒥᑦ 3-ᒥᒃ ᐱᑎᑦᓯᔪᖅ. HHHT 3-ᒥᑦ 2-ᒥᒃ. HTHT 2-ᒥᑦ 0-ᒥᒃ. 14 ᐃᓘᓐᓇᖏᑦ ᖃᐅᔨᓴᖅᑕᐅᒍᑎᒃ: ᐱᖓᓲᔪᖅᑐᑦ 0-ᒥᒃ ᐱᑎᑦᓯᔪᑦ, ᓯᑕᒪᑦ 1/2-ᒥᒃ, ᐊᑕᐅᓯᖅ 2/3-ᒥᒃ ᐱᖓᓱᓪᓗ 1-ᒥᒃ (HHHH, THHH ᐊᒻᒪ TTHH).
ᐃᓚᖏᑦ ᑲᑎᓗᒋᑦ: 4 × 1/2 + 2/3 + 3 = 17/3. 14-ᒧᑦ ᐊᕕᒡᓗᒍ: 17/42 ≈ 40.5%.
ᐃᒋᑕᐅᓂᖅ ᓇᓕᐊᑐᐃᓐᓇᖅ ᑐᓄᖓᓂᒃ ᐱᐅᒋᓂᖅᓴᐅᙱᑦᑐᖅ; ᑕᒪᒃᑭᑦ 50–50. ᓴᖑᓯᒪᓂᖅ ᑐᓪᓕᖅᑲᑕᐅᔪᑦ ᐊᑕᐅᓯᐊᓗᒃ ᐊᑕᐅᓯᐊᓗᒃ ᐊᔾᔨᒌᒃᑎᑕᐅᓂᖏᓐᓂᑦ ᐱᔪᖅ: ᑐᓪᓕᖅᑲᑕᐅᔪᑦ ᓂᐊᖁᖏᓐᓂᒃ ᐃᑦᑕᕐᓂᑦᑐᑦ ᐊᒥᓱᓂᒃ ᑕᑯᔭᒃᓴᓂᒃ ᐱᖃᕐᒪᑕ ᐊᑕᐅᑦᑎᒃ ᑭᓯᐊᓂ ᓈᓴᖅᑕᐅᔪᑦ, ᑐᓪᓕᖅᑲᑕᐅᔪᑦ ᐊᑕᐅᓯᐊᓗᒻᒥᒃ ᐱᖃᖅᑐᑦ ᐊᔾᔨᖏᑦ. ᒥᓚ ᓴᓐᕼᐅᕐᕼᐅᓗ (Miller and Sanjurjo, 2018) ᓇᓗᓇᐃᖅᓯᔪᑦ 1985-ᒥ ᐆᓇᖅᑐᖅ ᐊᒡᒐ ᖃᐅᔨᓴᕐᓂᖅ ᐹᔅᑭᑦᐹᓪ ᐱᙳᐊᖅᑏᖏᓐᓂᒃ ᑕᒪᑐᒧᖓ ᓇᓂᓯᓯᒪᒻᒪᑦ, ᑕᐃᒪ ᐱᙳᐊᖅᑏᑦ ᐱᔪᓐᓇᕐᓂᐅᑉ ᐱᑎᑦᓯᔭᖓᓂᑦ ᑐᓪᓕᖅᑲᑕᐅᑦᑎᐊᓂᖅᓴᐅᙱᑦᑑᔮᖅᑐᑦ ᓱᓕᓪᓚᕆᒃᑐᒥᒃ ᑐᓪᓕᖅᑲᑕᐅᑦᑎᐊᓂᖅᓴᐅᓯᒪᔪᑦ ᐃᓚᓐᓂᒃᑯᑦ.
ᐃᓪᓗᕐᓇᖅᑐᖅ ᐊᔾᔨᓕᐊᖅ
ᐃᓚᒋᐊᖅᓯᓂᖅ, ᐋᖅᑭᒃᓯᓂᐅᙱᑦᑐᖅ. ᐊᒃᓴᓗᐊᖅ ᐊᑭᓕᒋᐊᖃᙱᑦᑐᖅ.
ᐱᕝᕕᒋᔭᐅᔪᑦ
- Feller, W. An Introduction to Probability Theory and Its Applications, Vol. 1.
- Schwartz, D. G. Roll the Bones: The History of Gambling.
- Ethier, S. N. The Doctrine of Chances: Probabilistic Aspects of Gambling.
- Epstein, R. A. The Theory of Gambling and Statistical Logic, 2nd ed.
- Croson, R. & Sundali, J. “The gambler’s fallacy and the hot hand: empirical data from casinos.”
- Chen, D. L., Moskowitz, T. J. & Shue, K. “Decision making under the gambler’s fallacy: evidence from asylum judges, loan officers, and baseball umpires.”
- Clotfelter, C. T. & Cook, P. J. “The ‘gambler’s fallacy’ in lottery play.”
- Gilovich, T., Vallone, R. & Tversky, A. “The hot hand in basketball: on the misperception of random sequences.”
- Miller, J. B. & Sanjurjo, A. “Surprised by the hot hand fallacy? A truth in the law of small numbers.”
- Hacking, I. “The inverse gambler’s fallacy: the argument from design. The anthropic principle applied to Wheeler universes.”