IDEAS home Printed from https://ideas.repec.org/p/cpr/ceprdp/13723.html
   My bibliography  Save this paper

Persuasion on Networks

Author

Listed:
  • Sonin, Konstantin
  • Egorov, Georgy

Abstract

We analyze persuasion in a model, in which each receiver might buy a direct access to the sender's signal or to rely on her network connections to get it. For the sender, a higher slant increases the impact per direct receiver, yet diminishes the willingness of agents to receive information. Contrary to naive intuition, the optimal propaganda might target peripheral, rather than centrally-located agents, and is at its maximum levels when the probability that information flows between agents is close to zero or nearly one, but not in-between. The impact of the network density depends on this probability as well.

Suggested Citation

  • Sonin, Konstantin & Egorov, Georgy, 2019. "Persuasion on Networks," CEPR Discussion Papers 13723, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:13723
    as

    Download full text from publisher

    File URL: https://cepr.org/publications/DP13723
    Download Restriction: CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Heng Chen & Yang K. Lu & Wing Suen, 2016. "The Power Of Whispers: A Theory Of Rumor, Communication, And Revolution," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 57, pages 89-116, February.
    2. Ina Taneva, 2019. "Information Design," American Economic Journal: Microeconomics, American Economic Association, vol. 11(4), pages 151-185, November.
    3. Kolotilin, Anton, 2018. "Optimal information disclosure: a linear programming approach," Theoretical Economics, Econometric Society, vol. 13(2), May.
    4. Venkatesh Bala & Sanjeev Goyal, 1998. "Learning from Neighbours," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 595-621.
    5. Chan, Jimmy & Gupta, Seher & Li, Fei & Wang, Yun, 2019. "Pivotal persuasion," Journal of Economic Theory, Elsevier, vol. 180(C), pages 178-202.
      • Jimmy Chan & Seher Gupta & Fei Li & Yun Wang, 2018. "Pivotal Persuasion," Working Papers 2018-11-03, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    6. repec:hal:pseose:halshs-01245557 is not listed on IDEAS
    7. Maja Adena & Ruben Enikolopov & Maria Petrova & Veronica Santarosa & Ekaterina Zhuravskaya, 2015. "Radio and the Rise of The Nazis in Prewar Germany," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(4), pages 1885-1939.
    8. repec:nas:journl:v:115:y:2018:p:e6996-e7004 is not listed on IDEAS
    9. Ludmila Matyskova, 2018. "Bayesian Persuasion with Costly Information Acquisition," CERGE-EI Working Papers wp614, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    10. David Yanagizawa-Drott, 2014. "Propaganda and Conflict: Evidence from the Rwandan Genocide," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(4), pages 1947-1994.
    11. Luis Rayo & Ilya Segal, 2010. "Optimal Information Disclosure," Journal of Political Economy, University of Chicago Press, vol. 118(5), pages 949-987.
    12. Michael Suk-Young Chwe, 2000. "Communication and Coordination in Social Networks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 67(1), pages 1-16.
    13. 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.
    14. Isabelle Brocas & Juan D. Carrillo, 2007. "Influence through ignorance," RAND Journal of Economics, RAND Corporation, vol. 38(4), pages 931-947, December.
    15. Matthew Gentzkow & Emir Kamenica, 2014. "Costly Persuasion," American Economic Review, American Economic Association, vol. 104(5), pages 457-462, May.
    16. Venkatesh Bala & Sanjeev Goyal, 2000. "A Noncooperative Model of Network Formation," Econometrica, Econometric Society, vol. 68(5), pages 1181-1230, September.
    17. Michael Ostrovsky & Michael Schwarz, 2010. "Information Disclosure and Unraveling in Matching Markets," American Economic Journal: Microeconomics, American Economic Association, vol. 2(2), pages 34-63, May.
    18. Ricardo Alonso & Odilon Câmara, 2016. "Persuading Voters," American Economic Review, American Economic Association, vol. 106(11), pages 3590-3605, November.
    19. Paul R. Milgrom, 1981. "Good News and Bad News: Representation Theorems and Applications," Bell Journal of Economics, The RAND Corporation, vol. 12(2), pages 380-391, Autumn.
    20. Lorentzen, Peter L., 2013. "Regularizing Rioting: Permitting Public Protest in an Authoritarian Regime," Quarterly Journal of Political Science, now publishers, vol. 8(2), pages 127-158, February.
    21. King, Gary & Pan, Jennifer & Roberts, Margaret E., 2017. "How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, Not Engaged Argument," American Political Science Review, Cambridge University Press, vol. 111(3), pages 484-501, August.
    22. Kolotilin, Anton, 2015. "Experimental design to persuade," Games and Economic Behavior, Elsevier, vol. 90(C), pages 215-226.
    23. Zenou, Yves & Jackson, Matthew O. & Rogers, Brian, 2016. "Networks: An economic perspective," CEPR Discussion Papers 11452, C.E.P.R. Discussion Papers.
    24. Shanker Satyanath & Nico Voigtländer & Hans-Joachim Voth, 2017. "Bowling for Fascism: Social Capital and the Rise of the Nazi Party," Journal of Political Economy, University of Chicago Press, vol. 125(2), pages 478-526.
    25. Yuyu Chen & David Y. Yang, 2019. "The Impact of Media Censorship: 1984 or Brave New World?," American Economic Review, American Economic Association, vol. 109(6), pages 2294-2332, June.
    26. Yun Wang, 2015. "Bayesian Persuasion with Multiple Receivers," Working Papers 2015-03-24, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    27. Calvó-Armengol, Antoni & , & ,, 2015. "Communication and influence," Theoretical Economics, Econometric Society, vol. 10(2), May.
    28. King, Gary & Pan, Jennifer & Roberts, Margaret E., 2013. "How Censorship in China Allows Government Criticism but Silences Collective Expression," American Political Science Review, Cambridge University Press, vol. 107(2), pages 326-343, May.
    29. Egorov, Georgy & Guriev, Sergei & Sonin, Konstantin, 2009. "Why Resource-poor Dictators Allow Freer Media: A Theory and Evidence from Panel Data," American Political Science Review, Cambridge University Press, vol. 103(4), pages 645-668, November.
    30. Sims, Christopher A., 2003. "Implications of rational inattention," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 665-690, April.
    31. Akbarpour, Mohammad & Malladi, Suraj & Saberi, Amin, 2018. "Just a Few Seeds More: Value of Network Information for Diffusion," Research Papers 3678, Stanford University, Graduate School of Business.
    32. Michael Spence, 1973. "Job Market Signaling," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 87(3), pages 355-374.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sergei Guriev & Daniel Treisman, 2019. "Informational Autocrats," Journal of Economic Perspectives, American Economic Association, vol. 33(4), pages 100-127, Fall.
    2. Itai Arieli & Yakov Babichenko & Fedor Sandomirskiy, 2022. "Bayesian Persuasion with Mediators," Papers 2203.04285, arXiv.org, revised Sep 2022.
    3. Yifan Zhang, 2024. "Persuasion in Networks: Can the Sender Do Better than Using Public Signals?," Papers 2404.18965, arXiv.org, revised Dec 2024.
    4. Toygar T. Kerman & Anastas P. Tenev, 2024. "Pitfalls of Information Spillovers in Persuasion," CERGE-EI Working Papers wp772, The Center for Economic Research and Graduate Education - Economics Institute, Prague.

    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. Hedlund, Jonas, 2017. "Bayesian persuasion by a privately informed sender," Journal of Economic Theory, Elsevier, vol. 167(C), pages 229-268.
    2. Rosar, Frank, 2017. "Test design under voluntary participation," Games and Economic Behavior, Elsevier, vol. 104(C), pages 632-655.
    3. Dirk Bergemann & Stephen Morris, 2019. "Information Design: A Unified Perspective," Journal of Economic Literature, American Economic Association, vol. 57(1), pages 44-95, March.
    4. Chan, Jimmy & Gupta, Seher & Li, Fei & Wang, Yun, 2019. "Pivotal persuasion," Journal of Economic Theory, Elsevier, vol. 180(C), pages 178-202.
      • Jimmy Chan & Seher Gupta & Fei Li & Yun Wang, 2018. "Pivotal Persuasion," Working Papers 2018-11-03, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    5. Kolotilin, Anton, 2015. "Experimental design to persuade," Games and Economic Behavior, Elsevier, vol. 90(C), pages 215-226.
    6. Georgy Egorov & Konstantin Sonin, 2024. "The Political Economics of Non-democracy," Journal of Economic Literature, American Economic Association, vol. 62(2), pages 594-636, June.
    7. Alonso, Ricardo & Câmara, Odilon, 2016. "Bayesian persuasion with heterogeneous priors," Journal of Economic Theory, Elsevier, vol. 165(C), pages 672-706.
    8. Vladimir Asriyan & Dana Foarta & Victoria Vanasco, 2023. "The Good, the Bad, and the Complex: Product Design with Imperfect Information," American Economic Journal: Microeconomics, American Economic Association, vol. 15(2), pages 187-226, May.
    9. Tsakas, Elias & Tsakas, Nikolas, 2021. "Noisy persuasion," Games and Economic Behavior, Elsevier, vol. 130(C), pages 44-61.
    10. Jackson, Matthew O. & Zenou, Yves, 2015. "Games on Networks," Handbook of Game Theory with Economic Applications,, Elsevier.
    11. Asriyan, Vladimir & Foarta, Dana & Vanasco, Victoria, 2018. "Strategic Complexity When Seeking Approval," Research Papers 3615, Stanford University, Graduate School of Business.
    12. Anton Kolotilin & Tymofiy Mylovanov & Andriy Zapechelnyuk & Ming Li, 2017. "Persuasion of a Privately Informed Receiver," Econometrica, Econometric Society, vol. 85(6), pages 1949-1964, November.
    13. Ozan Candogan & Philipp Strack, 2021. "Optimal Disclosure of Information to a Privately Informed Receiver," Papers 2101.10431, arXiv.org, revised Jan 2022.
    14. Arianna Degan & Ming Li, 2021. "Persuasion with costly precision," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 72(3), pages 869-908, October.
    15. Ekaterina Zhuravskaya & Maria Petrova & Ruben Enikolopov, 2020. "Political Effects of the Internet and Social Media," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 415-438, August.
    16. Boxell, Levi & Steinert-Threlkeld, Zachary, 2022. "Taxing dissent: The impact of a social media tax in Uganda," World Development, Elsevier, vol. 158(C).
    17. Kerman, Toygar & Herings, P. Jean-Jacques & Karos, Dominik, 2020. "Persuading Strategic Voters," Research Memorandum 004, Maastricht University, Graduate School of Business and Economics (GSBE).
    18. Emir Kamenica & Matthew Gentzkow, 2011. "Bayesian Persuasion," American Economic Review, American Economic Association, vol. 101(6), pages 2590-2615, October.
    19. Florian Hoffmann & Roman Inderst & Marco Ottaviani, 2020. "Persuasion Through Selective Disclosure: Implications for Marketing, Campaigning, and Privacy Regulation," Management Science, INFORMS, vol. 66(11), pages 4958-4979, November.
    20. Inés Moreno de Barreda & Gilat Levy & Ronny Razin, 2017. "Persuasion with Correlation Neglect: Media Power via Correlation of News Content," Economics Series Working Papers 836, University of Oxford, Department of Economics.

    More about this item

    Keywords

    Propaganda; Networks; Bayesian persuasion; Percolation;
    All these keywords.

    JEL classification:

    • P16 - Political Economy and Comparative Economic Systems - - Capitalist Economies - - - Capitalist Institutions; Welfare State
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media

    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:cpr:ceprdp:13723. 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: the person in charge (email available below). General contact details of provider: https://www.cepr.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.