The natural selection of prediction heuristics: Anchoring and adjustment versus representativeness

Benjamin Czaczkes, Yoav Ganzach

نتاج البحث: نشر في مجلةمقالةمراجعة النظراء

18 اقتباسات (Scopus)

ملخص

There are several heuristics which people use in making numerical predictions and these heuristics compete for the determination of prediction output. Some of them (e.g. representativeness) lead to excessively extreme predictions while others (e.g. anchoring and adjustment) lead to regressive (and even over-regressive) predictions. In this paper we study the competition between these two heuristics by varying the representation of predictor and outcome. The results indicate that factors which facilitate reliance on representativeness (e.g. compatibility between predictor and outcome) indeed lead to an increase in extremity, while factors that facilitate reliance on anchoring and adjustment (e.g. increased salience of a potential anchor) lead to a decrease in extremity.

اللغة الأصليةالإنجليزيّة
الصفحات (من إلى)125-139
عدد الصفحات15
دوريةJournal of Behavioral Decision Making
مستوى الصوت9
رقم الإصدار2
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 1996
منشور خارجيًانعم

بصمة

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