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Engagement Maximization

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Listed:
  • Benjamin H'ebert
  • Weijie Zhong

Abstract

We investigate the management of information provision to maximize user engagement. A principal sequentially reveals signals to an agent who has a limited amount of information processing capacity and can choose to exit at any time. We identify a ``dilution'' strategy -- sending rare but highly informative signals -- that maximizes user engagement. The platform's engagement metric shapes the direction and magnitude of biases in provided information relative to a user-optimal benchmark. Even without intertemporal commitment, the platform replicates full-commitment revenue by inducing the user's belief to remain ``as uncertain as'' the prior until the rare, decisive signal arrives and induces stopping. We apply our results to two contexts: an ad-supported internet media platform and a teacher attempting to engage test-motivated students.

Suggested Citation

  • Benjamin H'ebert & Weijie Zhong, 2022. "Engagement Maximization," Papers 2207.00685, arXiv.org, revised Dec 2025.
    • Handle: RePEc:arx:papers:2207.00685
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    References listed on IDEAS

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    1. Matthew Gentzkow & Jesse M. Shapiro, 2006. "Media Bias and Reputation," Journal of Political Economy, University of Chicago Press, vol. 114(2), pages 280-316, April.
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    7. Hébert, Benjamin & Zhong, Weijie, 0. "Engagement maximization," Theoretical Economics, Econometric Society.
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    1. Hébert, Benjamin & Zhong, Weijie, 0. "Engagement maximization," Theoretical Economics, Econometric Society.

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    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D86 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Economics of Contract Law

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