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Learning in Repeated Interactions on Networks

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  • Wanying Huang
  • Philipp Strack
  • Omer Tamuz

Abstract

We study how long‐lived, rational agents learn in a social network. In every period, after observing the past actions of his neighbors, each agent receives a private signal, and chooses an action whose payoff depends only on the state. Since equilibrium actions depend on higher‐order beliefs, it is difficult to characterize behavior. Nevertheless, we show that regardless of the size and shape of the network, the utility function, and the patience of the agents, the speed of learning in any equilibrium is bounded from above by a constant that only depends on the private signal distribution.

Suggested Citation

  • Wanying Huang & Philipp Strack & Omer Tamuz, 2024. "Learning in Repeated Interactions on Networks," Econometrica, Econometric Society, vol. 92(1), pages 1-27, January.
    • Handle: RePEc:wly:emetrp:v:92:y:2024:i:1:p:1-27
    DOI: 10.3982/ECTA20806
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    References listed on IDEAS

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    1. Matan Harel & Elchanan Mossel & Philipp Strack & Omer Tamuz, 2021. "Rational Groupthink," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(1), pages 621-668.
      • Matan Harel & Elchanan Mossel & Philipp Strack & Omer Tamuz, 2014. "Rational Groupthink," Papers 1412.7172, arXiv.org, revised Jun 2020.
    2. Marco Ottaviani & Giuseppe Moscarini & Lones Smith, 1998. "Social learning in a changing world," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 11(3), pages 657-665.
    3. Venkatesh Bala & Sanjeev Goyal, 1998. "Learning from Neighbours," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 595-621.
    4. Xavier Vives, 1993. "How Fast do Rational Agents Learn?," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(2), pages 329-347.
    5. Daron Acemoglu & Munther A. Dahleh & Ilan Lobel & Asuman Ozdaglar, 2011. "Bayesian Learning in Social Networks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(4), pages 1201-1236.
    6. Heidhues, Paul & Rady, Sven & Strack, Philipp, 2015. "Strategic experimentation with private payoffs," Journal of Economic Theory, Elsevier, vol. 159(PA), pages 531-551.
    7. Hann-Caruthers, Wade & Martynov, Vadim V. & Tamuz, Omer, 2018. "The speed of sequential asymptotic learning," Journal of Economic Theory, Elsevier, vol. 173(C), pages 383-409.
    8. Gale, Douglas & Kariv, Shachar, 2003. "Bayesian learning in social networks," Games and Economic Behavior, Elsevier, vol. 45(2), pages 329-346, November.
    9. Krishna Dasaratha & Benjamin Golub & Nir Hak, 2018. "Learning from Neighbors about a Changing State," Papers 1801.02042, arXiv.org, revised Nov 2022.
    10. Pooya Molavi & Alireza Tahbaz‐Salehi & Ali Jadbabaie, 2018. "A Theory of Non‐Bayesian Social Learning," Econometrica, Econometric Society, vol. 86(2), pages 445-490, March.
    11. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    12. Dinah Rosenberg & Nicolas Vieille, 2019. "On the Efficiency of Social Learning," Econometrica, Econometric Society, vol. 87(6), pages 2141-2168, November.
    13. Itai Arieli & Yakov Babichenko & Stephan Muller & Farzad Pourbabaee & Omer Tamuz, 2023. "The Hazards and Benefits of Condescension in Social Learning," Papers 2301.11237, arXiv.org, revised Feb 2024.
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