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Congested Observational Learning

Author

Listed:
  • Eyster, E
  • Galeotti, A
  • Kartik, N
  • Rabin, M
Abstract
We study observational learning in environments with congestion costs: as more of one's predecessors choose an action, the payoff from choosing that action decreases. If congestion on either action can get so large that an agent would prefer to take the other action no matter his beliefs about the state, then herds cannot occur. To the extent that "switching" away from the more popular action also reveals some private information, social learning is improved. The absence of herding is not enough to guarantee complete learning, however, as information cascades can occur through perpetual but uninformative switching between actions. For bounded private beliefs, we provide conditions that guarantee complete learning and conditions that guarantee bounded learning. Congestion costs have ambiguous effects on welfare as measured by the proportion of agents who choose the superior action. 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, E & Galeotti, A & Kartik, N & Rabin, M, 2012. "Congested Observational Learning," Economics Discussion Papers 8948, University of Essex, Department of Economics.
    • Handle: RePEc:esx:essedp:8948
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    References listed on IDEAS

    as
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    Cited by:

    1. Zhang, Min, 2021. "Non-monotone social learning," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 565-579.
    2. Arieli, Itai, 2017. "Payoff externalities and social learning," Games and Economic Behavior, Elsevier, vol. 104(C), pages 392-410.
    3. Isaac Loh & Gregory Phelan, 2019. "Dimensionality And Disagreement: Asymptotic Belief Divergence In Response To Common Information," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 60(4), pages 1861-1876, November.
    4. Monzón, Ignacio, 2019. "Observational learning in large anonymous games," Theoretical Economics, Econometric Society, vol. 14(2), May.
    5. Cripps, Martin W. & Thomas, Caroline D., 2019. "Strategic experimentation in queues," Theoretical Economics, Econometric Society, vol. 14(2), May.
    6. Alexei Parakhonyak & Nick Vikander, 2016. "Inducing Herding with Capacity Constraints," Economics Series Working Papers 808, University of Oxford, Department of Economics.
    7. 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.
    8. Song, Yangbo & Zhang, Jiahua, 2020. "Social learning with coordination motives," Games and Economic Behavior, Elsevier, vol. 123(C), pages 81-100.
    9. Amir Ban & Moran Koren, 2020. "A Practical Approach to Social Learning," Papers 2002.11017, arXiv.org.
    10. Ali, S. Nageeb, 2018. "On the role of responsiveness in rational herds," Economics Letters, Elsevier, vol. 163(C), pages 79-82.
    11. Xuanye Wang, 2021. "Fragility of Confounded Learning," Papers 2106.07712, arXiv.org.
    12. Arieli, Itai & Koren, Moran & Smorodinsky, Rann, 2022. "The implications of pricing on social learning," Theoretical Economics, Econometric Society, vol. 17(4), November.
    13. Sander Heinsalu, 2019. "Herding driven by the desire to differ," Papers 1904.00454, arXiv.org.

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