Measuring and Modeling Attention

Posted: 18 Nov 2016

See all articles by Andrew Caplin

Andrew Caplin

New York University (NYU) - Department of Economics; National Bureau of Economic Research (NBER)

Date Written: October 2016


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

Caplin, Andrew, Measuring and Modeling Attention (October 2016). Annual Review of Economics, Vol. 8, pp. 379-403, 2016, Available at SSRN: or

Andrew Caplin (Contact Author)

New York University (NYU) - Department of Economics ( email )

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