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Measuring and Modeling Attention

Author

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  • Andrew Caplin

    (Department of Economics, New York University, New York, NY 10003)

Abstract

This article presents a selective review of economic research on attentional choice, taking an observation of Block & Marschak (1960) as its starting point. Because standard choice data conflate utilities and perception, they point out that it is inadequate for research in which attention is endogenous. The review focuses on their thesis that advances in our understanding of attention require modeling of novel choice-based data sets, and corresponding methods of measurement. By way of example, recent attentional research based on measuring and modeling state-dependent stochastic choice data is detailed. Next research steps in relation to strategic attention and the dynamics of learning are outlined. If the thesis of Block & Marschak is valid, engineering of new data sets will become an increasingly essential professional activity as attentional research advances.

Suggested Citation

  • Andrew Caplin, 2016. "Measuring and Modeling Attention," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 379-403, October.
    • Handle: RePEc:anr:reveco:v:8:y:2016:p:379-403
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    File URL: http://www.annualreviews.org/doi/10.1146/annurev-economics-080315-015417
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    More about this item

    Keywords

    revealed preference; rational inattention; Bayesian updating; behavioral economics; imperfect information; costly information processing;
    All these keywords.

    JEL classification:

    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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