IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2502.12660.html
   My bibliography  Save this paper

Collective Intelligence in Dynamic Networks

Author

Listed:
  • Florian Mudekereza

Abstract

We revisit DeGroot learning to examine the robustness of social learning in dynamic networks -- networks that evolve randomly over time. Dynamics have double-edged effects depending on social structure: while they can foster consensus and boost collective intelligence in "sparse" networks, they can have adverse effects such as slowing down the speed of learning and causing long-run disagreement in "well-connected" networks. Collective intelligence arises in dynamic networks when average influence and trust remain balanced as society grows. We also find that the initial social structure of a dynamic network plays a central role in shaping long-run beliefs. We then propose a robust measure of homophily based on the likelihood of the worst network fragmentation.

Suggested Citation

  • Florian Mudekereza, 2025. "Collective Intelligence in Dynamic Networks," Papers 2502.12660, arXiv.org, revised Jun 2025.
    • Handle: RePEc:arx:papers:2502.12660
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2502.12660
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Brandts, Jordi & Giritligil, Ayça Ebru & Weber, Roberto A., 2015. "An experimental study of persuasion bias and social influence in networks," European Economic Review, Elsevier, vol. 80(C), pages 214-229.
    2. Fudenberg, Drew & Lanzani, Giacomo, 2023. "Which misspecifications persist?," Theoretical Economics, Econometric Society, vol. 18(3), July.
    3. George J. Mailath & Larry Samuelson, 2020. "Learning under Diverse World Views: Model-Based Inference," American Economic Review, American Economic Association, vol. 110(5), pages 1464-1501, May.
    4. Matthew O. Jackson & Stephen M. Nei & Erik Snowberg & Leeat Yariv, 2022. "The Dynamics of Networks and Homophily," NBER Working Papers 30815, National Bureau of Economic Research, Inc.
    5. Harrison Hong & Jeremy C. Stein & Jialin Yu, 2007. "Simple Forecasts and Paradigm Shifts," Journal of Finance, American Finance Association, vol. 62(3), pages 1207-1242, June.
    6. Simone Cerreia-Vioglio & Roberto Corrao & Giacomo Lanzani, 2024. "Dynamic Opinion Aggregation: Long-Run Stability and Disagreement," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(3), pages 1406-1447.
    7. 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.
    8. Dasaratha, Krishna, 2020. "Distributions of centrality on networks," Games and Economic Behavior, Elsevier, vol. 122(C), pages 1-27.
    9. Abhijit Banerjee & Arun G Chandrasekhar & Esther Duflo & Matthew O Jackson, 2019. "Using Gossips to Spread Information: Theory and Evidence from Two Randomized Controlled Trials," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(6), pages 2453-2490.
    10. Santanu Chakraborty & B. V. Rao, 2001. "Convolution Powers of Probabilities on Stochastic Matrices," Journal of Theoretical Probability, Springer, vol. 14(2), pages 599-603, April.
    11. Krishna Dasaratha & Benjamin Golub & Nir Hak, 2023. "Learning from Neighbours about a Changing State," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(5), pages 2326-2369.
    12. 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.
    13. 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.
    14. A. Mukherjea & A. Nakassis, 2002. "On the Continuous Singularity of the Limit Distribution of Products of I.I.D. d×d Stochastic Matrices," Journal of Theoretical Probability, Springer, vol. 15(4), pages 903-918, October.
    15. , & , H. & ,, 2015. "Sampling best response dynamics and deterministic equilibrium selection," Theoretical Economics, Econometric Society, vol. 10(1), January.
    16. Lawrence F. Katz & Jeffrey R. Kling & Jeffrey B. Liebman, 2001. "Moving to Opportunity in Boston: Early Results of a Randomized Mobility Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(2), pages 607-654.
    17. Abhijit Banerjee & Emily Breza & Arun G. Chandrasekhar & Markus Mobius, 2021. "Naïve Learning with Uninformed Agents," American Economic Review, American Economic Association, vol. 111(11), pages 3540-3574, November.
    18. Abhijit Banerjee & Emily Breza & Arun G Chandrasekhar & Esther Duflo & Matthew O Jackson & Cynthia Kinnan, 2024. "Changes in Social Network Structure in Response to Exposure to Formal Credit Markets," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(3), pages 1331-1372.
    19. Geanakoplos, John D. & Polemarchakis, Heraklis M., 1982. "We can't disagree forever," Journal of Economic Theory, Elsevier, vol. 28(1), pages 192-200, October.
    20. S. Dhar & A. Mukherjea, 1997. "Convergence in Distribution of Products of I.I.D. Nonnegative Matrices," Journal of Theoretical Probability, Springer, vol. 10(2), pages 375-393, April.
    21. Veronika Grimm & Friederike Mengel, 2020. "Experiments on Belief Formation in Networks," Journal of the European Economic Association, European Economic Association, vol. 18(1), pages 49-82.
    22. H. Hennion & L. Hervé, 2008. "Stable Laws and Products of Positive Random Matrices," Journal of Theoretical Probability, Springer, vol. 21(4), pages 966-981, December.
    23. Arun G. Chandrasekhar & Horacio Larreguy & Juan Pablo Xandri, 2020. "Testing Models of Social Learning on Networks: Evidence From Two Experiments," Econometrica, Econometric Society, vol. 88(1), pages 1-32, January.
    24. Göran Högnäs & Arunava Mukherjea, 2003. "Maximal Homomorphic Group Image and Convergence of Convolution Sequences on a Semigroup," Journal of Theoretical Probability, Springer, vol. 16(4), pages 847-854, October.
    25. repec:hal:pseose:halshs-01157537 is not listed on IDEAS
    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. Rapanos, Theodoros, 2023. "What makes an opinion leader: Expertise vs popularity," Games and Economic Behavior, Elsevier, vol. 138(C), pages 355-372.
    2. Li, Wei & Tan, Xu, 2021. "Cognitively-constrained learning from neighbors," Games and Economic Behavior, Elsevier, vol. 129(C), pages 32-54.
    3. Buechel, Berno & Klößner, Stefan & Meng, Fanyuan & Nassar, Anis, 2023. "Misinformation due to asymmetric information sharing," Journal of Economic Dynamics and Control, Elsevier, vol. 150(C).
    4. Miguel Risco, 2025. "Network effects on information acquisition by DeGroot updaters," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 79(1), pages 201-234, February.
    5. Amir, Gideon & Arieli, Itai & Ashkenazi-Golan, Galit & Peretz, Ron, 2025. "Granular DeGroot dynamics – A model for robust naive learning in social networks," Journal of Economic Theory, Elsevier, vol. 223(C).
    6. Cátia Batista & Marcel Fafchamps & Pedro C Vicente, 2022. "Keep It Simple: A Field Experiment on Information Sharing among Strangers [Changing Saving and Investment Behavior: The Impact of Financial Literacy Training and Reminders on Micro-Businesses]," The World Bank Economic Review, World Bank, vol. 36(4), pages 857-888.
    7. Syngjoo Choi & Sanjeev Goyal & Frederic Moisan & Yu Yang Tony To, 2023. "Learning in Networks: An Experiment on Large Networks with Real-World Features," Management Science, INFORMS, vol. 69(5), pages 2778-2787, May.
    8. Marcel Fafchamps & Måns Söderbom & Monique van den Boogart, 2022. "Adoption with Social Learning and Network Externalities," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(6), pages 1259-1282, December.
    9. 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.
    10. Anufriev, Mikhail & Borissov, Kirill & Pakhnin, Mikhail, 2023. "Dissonance minimization and conversation in social networks," Journal of Economic Behavior & Organization, Elsevier, vol. 215(C), pages 167-191.
    11. Ghosh, Aniruddha & Khan, M. Ali, 2021. "On a diversity of perspectives and world views: Learning under Bayesian vis-á-vis DeGroot updating," Economics Letters, Elsevier, vol. 202(C).
    12. Simone Cerreia-Vioglio & Roberto Corrao & Giacomo Lanzani, 2020. "Robust Opinion Aggregation and its Dynamics," Working Papers 662, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    13. Arieli, Itai & Babichenko, Yakov & Shlomov, Segev, 2021. "Virtually additive learning," Journal of Economic Theory, Elsevier, vol. 197(C).
    14. Crès, Hervé & Tvede, Mich, 2022. "Aggregation of opinions in networks of individuals and collectives," Journal of Economic Theory, Elsevier, vol. 199(C).
    15. Cao, GangCheng & Fang, Debin & Wang, Pengyu, 2021. "The impacts of social learning on a real-time pricing scheme in the electricity market," Applied Energy, Elsevier, vol. 291(C).
    16. Benedikt V. Meylahn & Arnoud V. den Boer & Michel Mandjes, 2023. "Trusting: Alone and together," Papers 2303.01921, arXiv.org, revised Feb 2024.
    17. Wanying Huang & Philipp Strack & Omer Tamuz, 2024. "Learning in Repeated Interactions on Networks," Econometrica, Econometric Society, vol. 92(1), pages 1-27, January.
    18. Markus Mobius & Tuan Phan & Adam Szeidl, 2015. "Treasure Hunt: Social Learning in the Field," CEU Working Papers 2015_2, Department of Economics, Central European University.
    19. Dasaratha, Krishna & He, Kevin, 2020. "Network structure and naive sequential learning," Theoretical Economics, Econometric Society, vol. 15(2), May.
    20. Isabel Melguizo, 2019. "Homophily and the Persistence of Disagreement," The Economic Journal, Royal Economic Society, vol. 129(619), pages 1400-1424.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:2502.12660. 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.