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Congested observational learning

Author

Listed:
  • Eyster, Erik
  • Galeotti, Andrea
  • Kartik, Navin
  • Rabin, Matthew

Abstract

We study observational learning in environments with congestion costs: an agent’s payoff from choosing an action decreases as more predecessors choose that action. Herds cannot occur if congestion on every action can get so large that an agent prefers a different action regardless of his beliefs about the state. To the extent that switching away from the more popular action reveals private information, it improves learning. The absence of herding does not guarantee complete (asymptotic) learning, however, as information cascades can occur through perpetual but uninformative switching between actions. We provide conditions on congestion costs that guarantee complete learning and conditions that guarantee bounded learning. Learning can be virtually complete even if each agent has only an infinitesimal effect on congestion costs. We apply our results to markets where congestion costs arise through responsive pricing and to queuing problems where agents dislike waiting for service.

Suggested Citation

  • Eyster, Erik & Galeotti, Andrea & Kartik, Navin & Rabin, Matthew, 2014. "Congested observational learning," LSE Research Online Documents on Economics 58748, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:58748
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    File URL: https://researchonline.lse.ac.uk/id/eprint/58748/
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    Cited by:

    1. Monzón, Ignacio, 2019. "Observational learning in large anonymous games," Theoretical Economics, Econometric Society, vol. 14(2), May.
    2. Qiao‐Chu He & Ying‐Ju Chen & Rhonda Righter, 2020. "Learning with Projection Effects in Service Operations Systems," Production and Operations Management, Production and Operations Management Society, vol. 29(1), pages 90-100, January.
    3. Amir Ban & Moran Koren, 2020. "A Practical Approach to Social Learning," Papers 2002.11017, arXiv.org.
    4. Ali, S. Nageeb, 2018. "On the role of responsiveness in rational herds," Economics Letters, Elsevier, vol. 163(C), pages 79-82.
    5. Arieli, Itai, 2017. "Payoff externalities and social learning," Games and Economic Behavior, Elsevier, vol. 104(C), pages 392-410.
    6. Song, Yangbo & Zhang, Jiahua, 2020. "Social learning with coordination motives," Games and Economic Behavior, Elsevier, vol. 123(C), pages 81-100.
    7. Isabel Kaluza & Guido Voigt & Friederike Paetz, 2024. "Empirical studies on the impact of booking status on customers’ choice behavior in online appointment systems," Journal of Business Economics, Springer, vol. 94(2), pages 187-224, February.
    8. Sander Heinsalu, 2019. "Herding driven by the desire to differ," Papers 1904.00454, arXiv.org.
    9. Xuanye Wang, 2021. "Fragility of Confounded Learning," Papers 2106.07712, arXiv.org.
    10. Arieli, Itai & Koren, Moran & Smorodinsky, Rann, 2022. "The implications of pricing on social learning," Theoretical Economics, Econometric Society, vol. 17(4), November.
    11. Zhang, Min, 2021. "Non-monotone social learning," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 565-579.
    12. Cripps, Martin W. & Thomas, Caroline D., 2019. "Strategic experimentation in queues," Theoretical Economics, Econometric Society, vol. 14(2), May.
    13. Alexei Parakhonyak & Nick Vikander, 2016. "Inducing Herding with Capacity Constraints," Economics Series Working Papers 808, University of Oxford, Department of Economics.
    14. David Lagziel & Yevgeny Tsodikovich, 2023. "Second Opinions and the Humility Threshold," Working Papers 2305, Ben-Gurion University of the Negev, Department of Economics.

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    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics

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