| Abstract: | In this paper we demonstrate that intuitive numerical predictions can be somewhat regressive. This moderation of predictions is asymmetric: predictions are more regressive at low than at high values of the predictor. This pattern is analyzed in terms of the operation of multiple heuristics. The representativeness heuristic is responsible for predictions in which extremity of the predicted variable is matched to extremity of the predictor. Matching is modified by a variety of intuitions that promote moderation per se; we lump these together under the heading of weak regressiveness. Third is leniency, a heuristic suggesting that the higher the uncertainty, the more positive should be the predictions. The first experiment demonstrates leniency in isolation from the other heuristics: in multivariate prediction, inconsistent predictors yield more positive predictions. Experiment 2 demonstrates asymmetric regression in a situation where all three heuristics are assumed to have effects. The third experiment exhibits leniency in the context of explanation of regression phenomena (rather than in numerical prediction). The final experiment explores the relation between the three heuristics and experience with multiple determination (Ganzach & Krantz, in press). It demonstrates increased moderation of predictions when subjects are required to generate a predicted value of an intermediate variable, for example, when the prediction of GPA from Intelligence is made subsequent to a prediction of Motivation. |
