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Dynamics of information exchange in endogenous social networks


  • Acemoglu, Daron

    () (Department of Economics, Massachusetts Institute of Technology)

  • Bimpikis, Kostas

    () (Graduate School of Business, Stanford University)

  • Ozdaglar, Asuman

    () (Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology)


We develop a model of information exchange through communication and investigate its implications for information aggregation in large societies. An \textit{underlying state} determines payoffs from different actions. Agents decide which others to form a costly \textit{communication link} with, incurring the associated cost. After receiving a \textit{private signal} correlated with the underlying state, they exchange information over the induced \textit{communication network} until taking an (irreversible) action. We define \textit{asymptotic learning} as the fraction of agents taking the correct action converging to one as a society grows large. Under truthful communication, we show that asymptotic learning occurs if (and under some additional conditions, also only if) in the induced communication network most agents are a short distance away from ``information hubs'', which receive and distribute a large amount of information. Asymptotic learning therefore requires information to be aggregated in the hands of a few agents. We also show that while truthful communication may not always be a best response, it is an equilibrium when the communication network induces asymptotic learning. Moreover, we contrast equilibrium behavior with a socially optimal strategy profile, i.e., a profile that maximizes aggregate welfare. We show that when the network induces asymptotic learning, equilibrium behavior leads to maximum aggregate welfare, but this may not be the case when asymptotic learning does not occur. We then provide a systematic investigation of what types of cost structures and associated social cliques (consisting of groups of individuals linked to each other at zero cost, such as friendship networks) ensure the emergence of communication networks that lead to asymptotic learning. Our result shows that societies with too many and sufficiently large social cliques do not induce asymptotic learning, because each social clique would have sufficient information by itself, making communication with others relatively unattractive. Asymptotic learning results either if social cliques are not too large, in which case communication across cliques is encouraged, or if there exist very large cliques that act as information hubs.

Suggested Citation

  • Acemoglu, Daron & Bimpikis, Kostas & Ozdaglar, Asuman, 2014. "Dynamics of information exchange in endogenous social networks," Theoretical Economics, Econometric Society, vol. 9(1), January.
  • Handle: RePEc:the:publsh:1204

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    References listed on IDEAS

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    Cited by:

    1. Junjie Zhou & Ying-Ju Chen, 2014. "Sequential selling and information dissemination in the presence of network effects," Working Papers 14-04, NET Institute.
    2. Sgrignoli, P. & Agliari, E. & Burioni, R. & Schianchi, A., 2015. "Instability and network effects in innovative markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 108(C), pages 260-271.
    3. Battiston, Pietro & Stanca, Luca, 2015. "Boundedly rational opinion dynamics in social networks: Does indegree matter?," Journal of Economic Behavior & Organization, Elsevier, vol. 119(C), pages 400-421.
    4. Arifovic, Jasmina & Eaton, B. Curtis & Walker, Graeme, 2015. "The coevolution of beliefs and networks," Journal of Economic Behavior & Organization, Elsevier, vol. 120(C), pages 46-63.
    5. Wuggenig, Mirjam, 2015. "Learning faster or more precisely? Strategic experimentation in networks," Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113196, Verein für Socialpolitik / German Economic Association.
    6. Bar Ifrach & Costis Maglaras & Marco Scarsini, 2011. "Monopoly Pricing in the Presence of Social Learning," Working Papers 11-11, NET Institute, revised Nov 2011.
    7. Garcia, Daniel, 2012. "Communication and Information Acquisition in Networks," MPRA Paper 55481, University Library of Munich, Germany, revised 24 Apr 2014.
    8. Andrei, Daniel & Cujean, Julien, 2017. "Information percolation, momentum and reversal," Journal of Financial Economics, Elsevier, vol. 123(3), pages 617-645.
    9. Aymanns, Christoph & Georg, Co-Pierre, 2015. "Contagious synchronization and endogenous network formation in financial networks," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 273-285.
    10. Rune Dahl Fitjar & Martin Gjelsvik, 2017. "Why do firms collaborate with local universities?," Papers in Evolutionary Economic Geography (PEEG) 1732, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Dec 2017.
    11. Pietro Battiston & Luca Stanca, 2014. "Boundedly Rational Opinion Dynamics in Directed Social Networks: Theory and Experimental Evidence," Working Papers 267, University of Milano-Bicocca, Department of Economics, revised Jan 2014.
    12. Daniel Andrei & Bruce Carlin & Michael Hasler, 2014. "Model Disagreement and Economic Outlook," NBER Working Papers 20190, National Bureau of Economic Research, Inc.
    13. Syngjoo Choi & Edoardo Gallo & Shachar Kariv, 2015. "Networks in the laboratory," Cambridge Working Papers in Economics 1551, Faculty of Economics, University of Cambridge.
    14. David Goldbaum, 2016. "Networks formation to assist decision making," Working Paper Series 37, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    15. Wuggenig, Mirjam, 2014. "Learning faster or more precisely? Strategic experimentation in networks," Discussion Paper Series of SFB/TR 15 Governance and the Efficiency of Economic Systems 485, Free University of Berlin, Humboldt University of Berlin, University of Bonn, University of Mannheim, University of Munich.
    16. Carlos Lever Guzmán, 2010. "Strategic Spending in Voting Competitions with Social Networks," Working Papers 2010-16, Banco de México.
    17. AJ Bostian & David Goldbaum, 2016. "Emergent Coordination among Competitors," Working Paper Series 36, Economics Discipline Group, UTS Business School, University of Technology, Sydney.

    More about this item


    Information aggregation; learning; search; social networks;

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation


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