IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2412.07061.html
   My bibliography  Save this paper

Network and timing effects in social learning

Author

Listed:
  • Wade Hann-Caruthers
  • Minghao Pan
  • Omer Tamuz

Abstract

We consider a group of agents who can each take an irreversible costly action whose payoff depends on an unknown state. Agents learn about the state from private signals, as well as from past actions of their social network neighbors, which creates an incentive to postpone taking the action. We show that outcomes depend on network structure: on networks with a linear structure patient agents do not converge to the first-best action, while on regular directed tree networks they do.

Suggested Citation

  • Wade Hann-Caruthers & Minghao Pan & Omer Tamuz, 2024. "Network and timing effects in social learning," Papers 2412.07061, arXiv.org.
    • Handle: RePEc:arx:papers:2412.07061
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2412.07061
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Luciano Pomatto & Philipp Strack & Omer Tamuz, 2018. "The Cost of Information: The Case of Constant Marginal Costs," Papers 1812.04211, arXiv.org, revised Feb 2023.
    2. Itai Arieli & Manuel Mueller-Frank, 2019. "Multidimensional Social Learning," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(3), pages 913-940.
    3. Luciano Pomatto & Philipp Strack & Omer Tamuz, 2023. "The Cost of Information: The Case of Constant Marginal Costs," American Economic Review, American Economic Association, vol. 113(5), pages 1360-1393, May.
    4. Grenadier, Steven R, 1999. "Information Revelation through Option Exercise," The Review of Financial Studies, Society for Financial Studies, vol. 12(1), pages 95-129.
    5. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    6. Simon Board & Moritz Meyer‐ter‐Vehn, 2021. "Learning Dynamics in Social Networks," Econometrica, Econometric Society, vol. 89(6), pages 2601-2635, November.
    7. Gadi Fibich & Ro'i Gibori, 2010. "Aggregate Diffusion Dynamics in Agent-Based Models with a Spatial Structure," Operations Research, INFORMS, vol. 58(5), pages 1450-1468, October.
    8. 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.
    9. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    10. Elchanan Mossel & Allan Sly & Omer Tamuz, 2015. "Strategic Learning and the Topology of Social Networks," Econometrica, Econometric Society, vol. 83(5), pages 1755-1794, September.
    11. Simon Board & Moritz Meyer-ter-Vehn, 2024. "Experimentation in Networks," American Economic Review, American Economic Association, vol. 114(9), pages 2940-2980, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arieli, Itai & Koren, Moran & Smorodinsky, Rann, 2022. "The implications of pricing on social learning," Theoretical Economics, Econometric Society, vol. 17(4), November.
    2. Itai Arieli & Fedor Sandomirskiy & Rann Smorodinsky, 2020. "On social networks that support learning," Papers 2011.05255, arXiv.org.
    3. Dasaratha, Krishna & He, Kevin, 2020. "Network structure and naive sequential learning," Theoretical Economics, Econometric Society, vol. 15(2), May.
    4. Alex Centeno, 2022. "A Structural Model for Detecting Communities in Networks," Papers 2209.08380, arXiv.org, revised Oct 2022.
    5. Daron Acemoglu & Ali Makhdoumi & Azarakhsh Malekian & Asuman Ozdaglar, 2017. "Fast and Slow Learning From Reviews," NBER Working Papers 24046, National Bureau of Economic Research, Inc.
    6. Abhijit Banerjee & Olivier Compte, 2024. "Consensus and Disagreement: Information Aggregation under (Not So) Naive Learning," Journal of Political Economy, University of Chicago Press, vol. 132(8), pages 2790-2829.
    7. Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046, January.
    8. Michel Grabisch & Agnieszka Rusinowska & Xavier Venel, 2019. "Diffusion in countably infinite networks," Documents de travail du Centre d'Economie de la Sorbonne 19017, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    9. Delia Coculescu & Médéric Motte & Huyên Pham, 2024. "Opinion dynamics in communities with major influencers and implicit social influence via mean-field approximation," Mathematics and Financial Economics, Springer, volume 18, number 7, February.
    10. Krishna Dasaratha & Benjamin Golub & Nir Hak, 2018. "Learning from Neighbors about a Changing State," Papers 1801.02042, arXiv.org, revised Nov 2022.
    11. Li, Feng & Du, Timon C. & Wei, Ying, 2023. "This is what’s in store for you: How online social learning affects product positioning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    12. Ding, Huihui & Pivato, Marcus, 2021. "Deliberation and epistemic democracy," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 138-167.
    13. Camilo García-Jimeno & Angel Iglesias & Pinar Yildirim, 2018. "Women, Rails and Telegraphs: An Empirical Study of Information Diffusion and Collective Action," NBER Working Papers 24495, National Bureau of Economic Research, Inc.
    14. Li, Wei & Tan, Xu, 2021. "Cognitively-constrained learning from neighbors," Games and Economic Behavior, Elsevier, vol. 129(C), pages 32-54.
    15. Ningyuan Chen & Anran Li & Kalyan Talluri, 2021. "Reviews and Self-Selection Bias with Operational Implications," Management Science, INFORMS, vol. 67(12), pages 7472-7492, December.
    16. Luca Braghieri, 2023. "Biased Decoding and the Foundations of Communication," CESifo Working Paper Series 10432, CESifo.
    17. Mueller-Frank, Manuel & Arieliy, Itai, 2015. "A General Model of Boundedly Rational Observational Learning: Theory and Experiment," IESE Research Papers D/1120, IESE Business School.
    18. Buechel, Berno & Hellmann, Tim & Klößner, Stefan, 2015. "Opinion dynamics and wisdom under conformity," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 240-257.
    19. Jakob Grazzini & Matteo G. Richiardi & Lisa Sella, 2013. "Analysis of Agent-based Models," LABORatorio R. Revelli Working Papers Series 135, LABORatorio R. Revelli, Centre for Employment Studies.
    20. Rusinowska, Agnieszka & Taalaibekova, Akylai, 2019. "Opinion formation and targeting when persuaders have extreme and centrist opinions," Journal of Mathematical Economics, Elsevier, vol. 84(C), pages 9-27.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2412.07061. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.