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Herding with costly information

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  • Ali, S. Nageeb

Abstract

This paper incorporates costly information into a model of observational learning. Individuals would like to avoid the cost of buying information and free-ride on the public history. The paper characterizes when learning is nevertheless complete. Necessary and sufficient conditions for complete learning follow from an elementary principle: a player purchases information only if it can influence her action. With a “coarse” action space, learning is complete if and only if for every cost c>0, a positive measure of types can acquire, at cost less than c, an experiment that can overturn the public history. With a “rich” action space, learning is complete if and only if for every cost c>0, a positive measure of types can acquire any informative signal at cost weakly less than c. The results are applied to financial markets to evaluate when markets are informationally efficient despite information being costly.

Suggested Citation

  • Ali, S. Nageeb, 2018. "Herding with costly information," Journal of Economic Theory, Elsevier, vol. 175(C), pages 713-729.
  • Handle: RePEc:eee:jetheo:v:175:y:2018:i:c:p:713-729
    DOI: 10.1016/j.jet.2018.02.009
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    Cited by:

    1. Aghamolla, Cyrus & Hashimoto, Tadashi, 2020. "Information arrival, delay, and clustering in financial markets with dynamic freeriding," Journal of Financial Economics, Elsevier, vol. 138(1), pages 27-52.
    2. Simon Board & Moritz Meyer‐ter‐Vehn, 2021. "Learning Dynamics in Social Networks," Econometrica, Econometric Society, vol. 89(6), pages 2601-2635, November.
    3. Dominik Naeher, 2022. "Technology Adoption Under Costly Information Processing," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(2), pages 699-753, May.
    4. Harry Pei, 2020. "Reputation Building under Observational Learning," Papers 2006.08068, arXiv.org, revised Nov 2020.
    5. Mira Frick & Ryota Iijima & Yuhta Ishii, 2020. "Misinterpreting Others and the Fragility of Social Learning," Econometrica, Econometric Society, vol. 88(6), pages 2281-2328, November.
    6. Sushil Bikhchandani & David Hirshleifer & Omer Tamuz & Ivo Welch, 2021. "Information Cascades and Social Learning," Papers 2105.11044, arXiv.org.
    7. Jacob Glazer & Ilan Kremer & Motty Perry, 2021. "The Wisdom of the Crowd When Acquiring Information Is Costly," Management Science, INFORMS, vol. 67(10), pages 6443-6456, October.
    8. Cunha, Douglas & Monte, Daniel, 2023. "Diversity Fosters Learning in Environments with Experimentation and Social Learning," MPRA Paper 117095, University Library of Munich, Germany.
    9. Bobkova, Nina & Mass, Helene, 2022. "Two-dimensional information acquisition in social learning," Journal of Economic Theory, Elsevier, vol. 202(C).
    10. Raphael L'evy & Marcin Pk{e}ski & Nicolas Vieille, 2022. "Stationary social learning in a changing environment," Papers 2201.02122, arXiv.org.
    11. Manxi Wu & Saurabh Amin & Asuman Ozdaglar, 2021. "Multi-agent Bayesian Learning with Best Response Dynamics: Convergence and Stability," Papers 2109.00719, arXiv.org.
    12. Jan Hązła & Ali Jadbabaie & Elchanan Mossel & M. Amin Rahimian, 2021. "Bayesian Decision Making in Groups is Hard," Operations Research, INFORMS, vol. 69(2), pages 632-654, March.
    13. Tian, Xin & Song, Yan & Luo, Chunlin & Zhou, Xiaoyang & Lev, Benjamin, 2021. "Herding behavior in supplier innovation crowdfunding: Evidence from Kickstarter," International Journal of Production Economics, Elsevier, vol. 239(C).

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    More about this item

    Keywords

    Social learning; Herding; Information acquisition; Responsiveness;
    All these keywords.

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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