IDEAS home Printed from https://ideas.repec.org/p/hal/journl/halshs-01720017.html
   My bibliography  Save this paper

Opinion formation and targeting when persuaders have extreme and centrist opinions

Author

Listed:
  • Agnieszka Rusinowska

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Akylai Taalaibekova

    (CORE - Center of Operation Research and Econometrics [Louvain] - UCL - Université Catholique de Louvain = Catholic University of Louvain, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

Abstract

We consider a model of competitive opinion formation in which three persuaders characterized by (possibly unequal) persuasion impacts try to influence opinions in a society of individuals embedded in a social network. Two of the persuaders have the extreme and opposite opinions, and the third one has the centrist opinion. Each persuader chooses one individual to target, i.e., he forms a link with the chosen individual in order to spread his own "point of view" in the society and to get the average long run opinion as close as possible to his own opinion. We examine the opinion convergence and consensus reaching in the society. We study the existence and characterization of pure strategy Nash equilibria in the game played by the persuaders with equal impacts. This characterization depends on influenceability and centrality (intermediacy) of the targets. We discuss the effect of the centrist persuader on the consensus and symmetric equilibria, compared to the framework with only two persuaders having the extreme opinions. When the persuasion impacts are unequal with one persuader having a sufficiently large impact, the game has only equilibria in mixed strategies.

Suggested Citation

  • Agnieszka Rusinowska & Akylai Taalaibekova, 2018. "Opinion formation and targeting when persuaders have extreme and centrist opinions," Post-Print halshs-01720017, HAL.
    • Handle: RePEc:hal:journl:halshs-01720017
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01720017
    as

    Download full text from publisher

    File URL: https://shs.hal.science/halshs-01720017/document
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Pajala, Tommi & Korhonen, Pekka & Malo, Pekka & Sinha, Ankur & Wallenius, Jyrki & Dehnokhalaji, Akram, 2018. "Accounting for political opinions, power, and influence: A Voting Advice Application," European Journal of Operational Research, Elsevier, vol. 266(2), pages 702-715.
    2. Förster, Manuel & Grabisch, Michel & Rusinowska, Agnieszka, 2013. "Anonymous social influence," Games and Economic Behavior, Elsevier, vol. 82(C), pages 621-635.
    3. Tsakas, Nikolas, 2017. "Diffusion by imitation: The importance of targeting agents," Journal of Economic Behavior & Organization, Elsevier, vol. 139(C), pages 118-151.
    4. Coralio Ballester & Antoni Calvó-Armengol & Yves Zenou, 2006. "Who's Who in Networks. Wanted: The Key Player," Econometrica, Econometric Society, vol. 74(5), pages 1403-1417, September.
    5. Venkatesh Bala & Sanjeev Goyal, 1998. "Learning from Neighbours," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 595-621.
    6. Yann Bramoullé & Andrea Galeotti & Brian Rogers, 2016. "The Oxford Handbook of the Economics of Networks," Post-Print hal-01447842, HAL.
    7. Grabisch, Michel & Rusinowska, Agnieszka, 2013. "A model of influence based on aggregation functions," Mathematical Social Sciences, Elsevier, vol. 66(3), pages 316-330.
    8. French, Simon, 1981. "Consensus of opinion," European Journal of Operational Research, Elsevier, vol. 7(4), pages 332-340, August.
    9. Yann Bramoullé & Andrea Galeotti & Brian Rogers, 2016. "The Oxford Handbook of the Economics of Networks," Post-Print hal-03572533, HAL.
    10. 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.
    11. 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.
    12. Banerjee, Abhijit & Fudenberg, Drew, 2004. "Word-of-mouth learning," Games and Economic Behavior, Elsevier, vol. 46(1), pages 1-22, January.
    13. Tsakas, Nikolas & Xefteris, Dimitrios, 2018. "Electoral competition with third party entry in the lab," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 121-134.
    14. 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.
    15. Benjamin Golub & Matthew O. Jackson, 2010. "Naïve Learning in Social Networks and the Wisdom of Crowds," American Economic Journal: Microeconomics, American Economic Association, vol. 2(1), pages 112-149, February.
    16. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    17. Andrea Galeotti & Benjamin Golub & Sanjeev Goyal, 2020. "Targeting Interventions in Networks," Econometrica, Econometric Society, vol. 88(6), pages 2445-2471, November.
    18. Pradeep Dubey & Rahul Garg & Bernard De Meyer, 2006. "Competing for Customers in a Social Network," Cowles Foundation Discussion Papers 1591, Cowles Foundation for Research in Economics, Yale University.
    19. Daron Acemoglu & Asuman Ozdaglar, 2011. "Opinion Dynamics and Learning in Social Networks," Dynamic Games and Applications, Springer, vol. 1(1), pages 3-49, March.
    20. Andrea Galeotti & Sanjeev Goyal, 2009. "Influencing the influencers: a theory of strategic diffusion," RAND Journal of Economics, RAND Corporation, vol. 40(3), pages 509-532, September.
    21. Kostas Bimpikis & Asuman Ozdaglar & Ercan Yildiz, 2016. "Competitive Targeted Advertising Over Networks," Operations Research, INFORMS, vol. 64(3), pages 705-720, June.
    22. Thomas R. Palfrey, 1984. "Spatial Equilibrium with Entry," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 51(1), pages 139-156.
    23. Peter M. DeMarzo & Dimitri Vayanos & Jeffrey Zwiebel, 2003. "Persuasion Bias, Social Influence, and Unidimensional Opinions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(3), pages 909-968.
    24. Grabisch, Michel & Rusinowska, Agnieszka, 2011. "A model of influence with a continuum of actions," Journal of Mathematical Economics, Elsevier, vol. 47(4-5), pages 576-587.
    25. Eklund, Patrik & Rusinowska, Agnieszka & De Swart, Harrie, 2007. "Consensus reaching in committees," European Journal of Operational Research, Elsevier, vol. 178(1), pages 185-193, April.
    26. Abhijit Banerjee & Arun G. Chandrasekhar & Esther Duflo & Matthew O. Jackson, 2012. "The Diffusion of Microfinance," NBER Working Papers 17743, National Bureau of Economic Research, Inc.
    27. Manuel Förster & Michel Grabisch & Agnieszka Rusinowska, 2012. "Ordered Weighted Averaging in Social Networks," Documents de travail du Centre d'Economie de la Sorbonne 12056, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    28. Michel Grabisch & Antoine Mandel & Agnieszka Rusinowska & Emily Tanimura, 2015. "Strategic influence in social networks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01158168, HAL.
    29. Bramoulle, Yann & Galeotti, Andrea & Rogers, Brian (ed.), 2016. "The Oxford Handbook of the Economics of Networks," OUP Catalogue, Oxford University Press, number 9780199948277.
    30. Maldonado, Felipe & Van Hentenryck, Pascal & Berbeglia, Gerardo & Berbeglia, Franco, 2018. "Popularity signals in trial-offer markets with social influence and position bias," European Journal of Operational Research, Elsevier, vol. 266(2), pages 775-793.
    31. Gale, Douglas & Kariv, Shachar, 2003. "Bayesian learning in social networks," Games and Economic Behavior, Elsevier, vol. 45(2), pages 329-346, November.
    32. 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.
    33. Kacprzyk, Janusz & Fedrizzi, Mario, 1988. "A `soft' measure of consensus in the setting of partial (fuzzy) preferences," European Journal of Operational Research, Elsevier, vol. 34(3), pages 316-325, March.
    34. Ozan Candogan & Kostas Bimpikis & Asuman Ozdaglar, 2012. "Optimal Pricing in Networks with Externalities," Operations Research, INFORMS, vol. 60(4), pages 883-905, August.
    35. Ellison, Glenn & Fudenberg, Drew, 1993. "Rules of Thumb for Social Learning," Journal of Political Economy, University of Chicago Press, vol. 101(4), pages 612-643, August.
    36. Anthony Downs, 1957. "An Economic Theory of Political Action in a Democracy," Journal of Political Economy, University of Chicago Press, vol. 65, pages 135-135.
    37. Nikolas Tsakas, 2014. "Optimal influence under observational learning," Gecomplexity Discussion Paper Series 4, Action IS1104 "The EU in the new complex geography of economic systems: models, tools and policy evaluation", revised Nov 2014.
    38. 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.
    39. Gómez, Daniel & Figueira, José Rui & Eusébio, Augusto, 2013. "Modeling centrality measures in social network analysis using bi-criteria network flow optimization problems," European Journal of Operational Research, Elsevier, vol. 226(2), pages 354-365.
    40. Forsythe, Robert & Rietz, Thomas & Myerson, Roger & Weber, Robert, 1996. "An Experimental Study of Voting Rules and Polls in Three-Candidate Elections," International Journal of Game Theory, Springer;Game Theory Society, vol. 25(3), pages 355-383.
    41. Molinero, Xavier & Riquelme, Fabián & Serna, Maria, 2015. "Cooperation through social influence," European Journal of Operational Research, Elsevier, vol. 242(3), pages 960-974.
    42. Acemoglu, Daron & Ozdaglar, Asuman & ParandehGheibi, Ali, 2010. "Spread of (mis)information in social networks," Games and Economic Behavior, Elsevier, vol. 70(2), pages 194-227, November.
    43. Daron Acemoğlu & Giacomo Como & Fabio Fagnani & Asuman Ozdaglar, 2013. "Opinion Fluctuations and Disagreement in Social Networks," Mathematics of Operations Research, INFORMS, vol. 38(1), pages 1-27, February.
    44. Michel Grabisch & Antoine Mandel & Agnieszka Rusinowska & Emily Tanimura, 2018. "Strategic Influence in Social Networks," Mathematics of Operations Research, INFORMS, vol. 43(1), pages 29-50, February.
    45. Mohammad Afshar & Masoud Asadpour, 2010. "Opinion Formation by Informed Agents," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(4), pages 1-5.
    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. 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).
    2. Akylai Taalaibekova, 2018. "Opinion formation in social networks," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 28(2), pages 85-108.

    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. Michel Grabisch & Antoine Mandel & Agnieszka Rusinowska & Emily Tanimura, 2018. "Strategic Influence in Social Networks," Mathematics of Operations Research, INFORMS, vol. 43(1), pages 29-50, February.
    2. Michel Grabisch & Agnieszka Rusinowska, 2020. "A Survey on Nonstrategic Models of Opinion Dynamics," Games, MDPI, vol. 11(4), pages 1-29, December.
    3. Grabisch, Michel & Poindron, Alexis & Rusinowska, Agnieszka, 2019. "A model of anonymous influence with anti-conformist agents," Journal of Economic Dynamics and Control, Elsevier, vol. 109(C).
    4. Akylai Taalaibekova, 2018. "Opinion formation in social networks," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 28(2), pages 85-108.
    5. Tsakas, Nikolas, 2017. "Diffusion by imitation: The importance of targeting agents," Journal of Economic Behavior & Organization, Elsevier, vol. 139(C), pages 118-151.
    6. 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.
    7. Sebastiano Della Lena, 2019. "Non-Bayesian Social Learning and the Spread of Misinformation in Networks," Working Papers 2019:09, Department of Economics, University of Venice "Ca' Foscari".
    8. 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.
    9. Foerster, Manuel, 2019. "Dynamics of strategic information transmission in social networks," Theoretical Economics, Econometric Society, vol. 14(1), January.
    10. 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.
    11. Michel Grabisch & Agnieszka Rusinowska, 2016. "Determining models of influence," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 26(2), pages 69-85.
    12. Förster, Manuel & Grabisch, Michel & Rusinowska, Agnieszka, 2013. "Anonymous social influence," Games and Economic Behavior, Elsevier, vol. 82(C), pages 621-635.
    13. Foerster, Manuel, 2018. "Finite languages, persuasion bias, and opinion fluctuations," Journal of Economic Behavior & Organization, Elsevier, vol. 149(C), pages 46-57.
    14. Germano, Fabrizio & Sobbrio, Francesco, 2020. "Opinion dynamics via search engines (and other algorithmic gatekeepers)," Journal of Public Economics, Elsevier, vol. 187(C).
    15. Jadbabaie, Ali & Molavi, Pooya & Sandroni, Alvaro & Tahbaz-Salehi, Alireza, 2012. "Non-Bayesian social learning," Games and Economic Behavior, Elsevier, vol. 76(1), pages 210-225.
    16. Azzimonti, Marina & Fernandes, Marcos, 2023. "Social media networks, fake news, and polarization," European Journal of Political Economy, Elsevier, vol. 76(C).
    17. Matjaž Steinbacher & Mitja Steinbacher, 2019. "Opinion Formation with Imperfect Agents as an Evolutionary Process," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 479-505, February.
    18. Ding, Huihui & Pivato, Marcus, 2021. "Deliberation and epistemic democracy," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 138-167.
    19. Jadbabaie, Ali & Kakhbod, Ali, 2019. "Optimal contracting in networks," Journal of Economic Theory, Elsevier, vol. 183(C), pages 1094-1153.
    20. Michel Grabisch & Agnieszka Rusinowska, 2016. "Determining influential models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01318081, HAL.

    More about this item

    Keywords

    social network; opinion formation; targeting; extreme and centrist persuaders; réseau social; formation d'opinion; consensus; ciblage; lobbying;
    All these keywords.

    JEL classification:

    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games

    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:hal:journl:halshs-01720017. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.