THE NATURAL-SELECTION OF PREDICTION HEURISTICS - ANCHORING AND ADJUSTMENT VERSUS REPRESENTATIVENESS

There are several heuristics which people use in making numerical pred ictions and these heuristics compete for the determination of predicti on 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 rep resentation of predictor and outcome. The results indicate that factor s 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 e xtremity.