IDEAS home Printed from https://ideas.repec.org/a/wsi/acsxxx/v13y2010i01ns0219525910002438.html
   My bibliography  Save this article

Opinion Dynamics And Communication Networks

Author

Listed:
  • SVEN BANISCH

    (Institute for Complexity Science (ICC), 1649-026 Lisbon, Portugal;
    Faculty of Media, Bauhaus–University Weimar, D-99421 Weimar, Germany;
    Research Unit on Complexity in Economics (UECE), ISEG, TULisbon, 1200-781 Lisbon, Portugal)

  • TANYA ARAÚJO

    (Institute for Complexity Science (ICC), 1649-026 Lisbon, Portugal;
    Research Unit on Complexity in Economics (UECE), ISEG, TULisbon, 1200-781 Lisbon, Portugal)

  • JORGE LOUÇÃ

    (Institute for Complexity Science (ICC), 1649-026 Lisbon, Portugal;
    Laboratory of Agent Modelling (LabMAg), ISCTE, 1649-026 Lisbon, Portugal)

Abstract

This paper examines the interplay of opinion exchange dynamics and communication network formation. An opinion formation procedure is introduced which is based on an abstract representation of opinions ask-dimensional bit-strings. Individuals interact if the difference in the opinion strings is below a defined similarity thresholddI. Depending ondI, different behavior of the population is observed: low values result in a state of highly fragmented opinions and higher values yield consensus. The first contribution of this research is to identify the values of parametersdIandk, such that the transition between fragmented opinions and homogeneity takes place. Then, we look at this transition from two perspectives: first by studying the group size distribution and second by analyzing the communication network that is formed by the interactions that take place during the simulation. The emerging networks are classified by statistical means and we find that nontrivial social structures emerge from simple rules for individual communication. Generating networks allows to compare model outcomes with real-world communication patterns.

Suggested Citation

  • Sven Banisch & Tanya Araújo & Jorge Louçã, 2010. "Opinion Dynamics And Communication Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 13(01), pages 95-111.
    • Handle: RePEc:wsi:acsxxx:v:13:y:2010:i:01:n:s0219525910002438
    DOI: 10.1142/S0219525910002438
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219525910002438
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0219525910002438?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Galam, Serge, 2004. "The dynamics of minority opinions in democratic debate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(1), pages 56-62.
    2. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    3. Tanya Araújo & Rui Vilela Mendes, 2007. "Innovation Success and Structural Change: An Abstract Agent Based Study," Working Papers Department of Economics 2007/25, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    4. Epstein, Joshua M., 2006. "Remarks on the Foundations of Agent-Based Generative Social Science," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 34, pages 1585-1604, Elsevier.
    5. F. Schweitzer & L. Behera, 2009. "Nonlinear voter models: the transition from invasion to coexistence," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 67(3), pages 301-318, February.
    6. Guillaume Deffuant & David Neau & Frederic Amblard & Gérard Weisbuch, 2000. "Mixing beliefs among interacting agents," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 3(01n04), pages 87-98.
    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. Christian Stummer & Lars Lüpke & Markus Günther, 2021. "Beaming market simulation to the future by combining agent-based modeling with scenario analysis," Journal of Business Economics, Springer, vol. 91(9), pages 1469-1497, November.

    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. Michael Neugart & Matteo G. Richiardi, 2012. "Agent-based models of the labor market," LABORatorio R. Revelli Working Papers Series 125, LABORatorio R. Revelli, Centre for Employment Studies.
    2. 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.
    3. Giacomo Vaccario & Mario V. Tomasello & Claudio J. Tessone & Frank Schweitzer, 2018. "Quantifying knowledge exchange in R&D networks: a data-driven model," Journal of Evolutionary Economics, Springer, vol. 28(3), pages 461-493, August.
    4. Mario V. Tomasello & Claudio J. Tessone & Frank Schweitzer, 2016. "A Model Of Dynamic Rewiring And Knowledge Exchange In R&D Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 19(01n02), pages 1-23, February.
    5. Sven Banisch & Eckehard Olbrich, 2021. "An Argument Communication Model of Polarization and Ideological Alignment," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 24(1), pages 1-1.
    6. Robin Nicole & Aleksandra Alori'c & Peter Sollich, 2020. "Fragmentation in trader preferences among multiple markets: Market coexistence versus single market dominance," Papers 2012.04103, arXiv.org, revised Aug 2021.
    7. Lu, Xi & Mo, Hongming & Deng, Yong, 2015. "An evidential opinion dynamics model based on heterogeneous social influential power," Chaos, Solitons & Fractals, Elsevier, vol. 73(C), pages 98-107.
    8. Yoo, Seung Han, 2014. "Learning a population distribution," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 188-201.
    9. Fernando Lozano & Jaime Lozano & Mario García, 2007. "An artificial economy based on reinforcement learning and agent based modeling," Documentos de Trabajo 3907, Universidad del Rosario.
    10. Giuseppe Attanasi & Samuele Centorrino & Ivan Moscati, 2011. "Double Auction Equilibrium and Efficiency in a Classroom Experimental Search Market," LERNA Working Papers 11.03.337, LERNA, University of Toulouse.
    11. Hang-Hyun Jo & Jeoung-Yoo Kim, 2012. "Competitive Targeted Marketing," ISER Discussion Paper 0834, Institute of Social and Economic Research, Osaka University.
    12. Gani Aldashev & Timoteo Carletti & Simone Righi, 2010. "Adaptive Expectations, Confirmatory Bias, and Informational Efficiency," Papers 1009.5075, arXiv.org.
    13. Leonie Geyer & Patrick Mellacher, 2024. "Simulating Party Competition in Dynamic Voter Distributions," Graz Economics Papers 2024-19, University of Graz, Department of Economics.
    14. Guillaume Deffuant & Ilaria Bertazzi & Sylvie Huet, 2018. "The Dark Side Of Gossips: Hints From A Simple Opinion Dynamics Model," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(06n07), pages 1-20, September.
    15. Feldman, Todd & Friedman, Daniel, 2008. "Humans, Robots and Market Crashes: A Laboratory Study ∗," Santa Cruz Department of Economics, Working Paper Series qt4kf382p6, Department of Economics, UC Santa Cruz.
    16. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
    17. G Jordan Maclay & Moody Ahmad, 2021. "An agent based force vector model of social influence that predicts strong polarization in a connected world," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-42, November.
    18. Daron Acemoglu & Asuman Ozdaglar, 2011. "Opinion Dynamics and Learning in Social Networks," Dynamic Games and Applications, Springer, vol. 1(1), pages 3-49, March.
    19. Tiwari, Mukesh & Yang, Xiguang & Sen, Surajit, 2021. "Modeling the nonlinear effects of opinion kinematics in elections: A simple Ising model with random field based study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    20. Shen, Meng & Li, Xiang & Lu, Yujie & Cui, Qingbin & Wei, Yi-Ming, 2021. "Personality-based normative feedback intervention for energy conservation," Energy Economics, Elsevier, vol. 104(C).

    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:wsi:acsxxx:v:13:y:2010:i:01:n:s0219525910002438. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/acs/acs.shtml .

    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.