According to this model, missing predictors are treated as an error term, with their unknown values replaced by central tendencies. We show that such moderation of prediction does not result from an abstract rule of regression to the mean rather, it can be explained by the named error model. 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.Ībstract In the experiments reported here, individuals with experience in a multivariate prediction setting showed considerable moderation of subsequent univariate predictions, compared to those without such experience. The final experiment explores the relation between the three heuristics and experience with multiple determination (Ganzach & Krantz, in press). The third experiment exhibits leniency in the context of explanation of regression phenomena (rather than in numerical prediction). Experiment 2 demonstrates asymmetric regression in a situation where all three heuristics are assumed to have effects. The first experiment demonstrates leniency in isolation from the other heuristics: in multivariate prediction, inconsistent predictors yield more positive predictions. Third is leniency, a heuristic suggesting that the higher the uncertainty, the more positive should be the predictions. Matching is modified by a variety of intuitions that promote moderation per se we lump these together under the heading of weak regressiveness. The representativeness heuristic is responsible for predictions in which extremity of the predicted variable is matched to extremity of the predictor. This pattern is analyzed in terms of the operation of multiple heuristics. This moderation of predictions is asymmetric: predictions are more regressive at low than at high values of the predictor. The robustness of hindsight bias is affirmed but it is strongly recommended that the reversal hypothesis offered by Mazursky and Ofir (1990) be accommodated.Ībstract In this paper we demonstrate that intuitive numerical predictions can be somewhat regressive. The alternative hypotheses and interpretations offered by them are discussed in detail, shown to be invalid, and ruled out. The present paper responds to Mark and Mellor's (1994) comments and also addresses general questions regarding the hindsight bias and its reversal. Instead, their judgments represent either the elimination of the bias or even its reversal. When asked to provide retrospective judgments, they would tend to provide judgments that are not in accordance with the hindsight bias. Mazursky and Ofir (1990) postulated that people may acknowledge their surprise at highly unexpected outcomes. Rather, the entry decisions of most of the subjects can be characterized by local adjustments to the outcome of the previous iteration of the same game along the lines suggested by anticipatory learning models.Ībstract Research conducted by Mazursky and Ofir (1990) suggests that the reaction to a surprising event may take the form of ‘I could not have expected it to happen’ rather than ‘I knew it all along,’ as implied by the hindsight bias. However, this coordination is not achieved via individual-level randomization. The results show a remarkable degree of tacit coordination that supports the equilibrium solution under the assumption of common risk aversion. We construct the Nash equilibrium solution to this game and then test it experimentally in the special case where each lottery yields only a single prize. In contrast to the widely studied paradigm of choice between gambles in individual decision making under risk in which the probabilities of the prizes are given, the probability of winning a prize in each of the lotteries in our study is known to decrease in the number of agents choosing this lottery. We study a class of interactive decision-making situations in which each agent must choose to participate in one of several lotteries with commonly known prizes.
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