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Memory and Probability*
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AI
DOI：10.1093/qje/qjac031.png)
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摘要
En 中文
In many economic decisions, people estimate probabilities, such as the likelihood that a risk materializes or that a job applicant will be a productive employee, by retrieving experiences from memory. We model this process based on two established regularities of selective recall: similarity and interference. We show that the similarity structure of a hypothesis and the way it is described (not just its objective probability) shape the recall of experiences and thus probability assessments. The model accounts for and reconciles a variety of empirical findings, such as overestimation of unlikely events when these are cued versus neglect of noncued ones, the availability heuristic, the representativeness heuristic, conjunction and disjunction fallacies, and over- versus underreaction to information in different situations. The model yields several new predictions, for which we find strong experimental support.
Keyword:
MODEL
SIMILARITY
FREQUENCY
FUTURE
INSURANCE
JUDGMENTS
UNPACKING
DECISION
BELIEFS
FAILURE