Attention Capture

Andrew Koh; Sivakorn Sanguanmoo

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arXiv · 2209.05570

Attention Capture

Andrew Koh·Sivakorn Sanguanmoo

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Abstract

We develop a unified analysis of how information captures attention. A decision maker (DM) faces a dynamic information structure and decides when to stop paying attention. We characterize the convex$\unicode{x2013}$order frontier and extreme points of feasible stopping times, as well as dynamic information structures which implement them. This delivers the form of optimal attentional capture as a function of the designer and DM's relative time preferences. Intertemporal commitment is unnecessary: sequentially optimal information structures always exist by inducing stochastic interim beliefs. We further analyze optimal attention capture under non instrumental value of information. Our results speak directly to the attention economy.

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Andrew Koh, Sivakorn Sanguanmoo. 2022-09-12. Attention Capture. https://arxiv.org/abs/2209.05570

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