Measuring and Modeling Attention

Andrew Caplin

    Research output: Contribution to journalReview article

    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.

    Original languageEnglish (US)
    Pages (from-to)379-403
    Number of pages25
    JournalAnnual Review of Economics
    Volume8
    DOIs
    StatePublished - Oct 31 2016

    Fingerprint

    Modeling
    Economic research
    Stochastic choice

    Keywords

    • Bayesian updating
    • Behavioral economics
    • Costly information processing
    • Imperfect information
    • Rational inattention
    • Revealed preference

    ASJC Scopus subject areas

    • Economics and Econometrics

    Cite this

    Measuring and Modeling Attention. / Caplin, Andrew.

    In: Annual Review of Economics, Vol. 8, 31.10.2016, p. 379-403.

    Research output: Contribution to journalReview article

    Caplin, Andrew. / Measuring and Modeling Attention. In: Annual Review of Economics. 2016 ; Vol. 8. pp. 379-403.
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