A parsimonious explanation of observed biases when forecasting one’s own performance

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• 作者：Sheik Meeran ; ^{s.meeran@bath.ac.uk" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Paul GoodwinAuthor Vitae ; Baris YalabikAuthor Vitae
• 关键词：Judgmental forecasting ; Metacognitive skills ; Regression effects ; Self-performance forecasting ; Anchoring and adjustment
• 刊名：International Journal of Forecasting
• 出版年：2016
• 出版时间：January-March 2016
• 年：2016
• 卷：32
• 期：1
• 页码：112-120
• 全文大小：402 K

文摘

Forecasting one’s own performance on tasks is important in a wide range of contexts. Over-forecasting can lead to an unresponsiveness to advice and feedback. In group forecasting, under-forecasting may lead individuals to discount valuable inputs that they could contribute. Research shows that those who perform relatively poorly in tasks tend to make predictions that are too high, while high performers tend to under-forecast their performances. Several explanations have been put forward for this ‘regressive forecasting’, such as a lack of metacognitive skills in poor performers and a false-consensus bias in high performers. Others claim that the bias is simply an artefact of regression. In this study, people were asked to forecast their performances on six multiple-choice tests. The results suggest that a simple explanation based on the anchoring and adjustment heuristic would account for the phenomenon, at least in part.