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Underestimating Learning by Doing
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AI
DOI：10.1257/mic.20240141.png)
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Abstract
En 中文
Many economic decisions, such as whether to invest in developing new skills, change professions, or purchase a technology, benefit from accurate estimation of skill acquisition. We examine the accuracy of such predictions by having study participants predict the speed at which they will master unfamiliar tasks. Across three studies involving two types of tasks and two levels of difficulty, we find systematic underestimation of learning, even after receiving feedback. In a fourth study, participants predicting others' performance showed significantly less underestimation, suggesting that projection bias- overreliance on immediate perceptions of effort and difficulty-may drive prediction errors.
Keywords:
KNOWLEDGE
JUDGMENTS
STABILITY
CURVES
CAUSAL
MODEL
BIAS
Journal
A
IF:
2.1
Papers:
37
Citations:
1.8K