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SELECTIVE ATTENTION AND LEARNING
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AI
DOI：10.1111/jeea.12104.png)
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Abstract
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What do we notice and how does this affect what we learn and come to believe? I present a model of an agent who learns to make forecasts on the basis of readily available information, but is selective as to which information he attends to: he chooses whether to attend as a function of current beliefs about whether such information is predictive. If the agent does not attend to some piece of information, it cannot be recalled at a later date. He uses Bayes' rule to update his beliefs given attended-to information, but does not attempt to fill in missing information. The model demonstrates how selective attention may lead the agent to persistently fail to recognize important empirical regularities, make systematically biased forecasts, and hold incorrect beliefs about the statistical relationship between variables. In addition, it identifies factors that make such errors more likely or persistent. The model is applied to shed light on stereotyping and discrimination, persistent learning failures and disagreement, and the process of discovery.
Keywords:
MEMORY
MODEL
SALIENCE
CHOICE
MATTER
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3.3
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1.5K
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AMERICAN ECONOMIC REVIEW
OAAI
AMERICAN ECONOMIC REVIEWIF11.6
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