IDEAS home Printed from https://ideas.repec.org/p/ise/isegwp/wp162009.html
   My bibliography  Save this paper

Opinion Dynamics and Communication Networks

Author

Listed:
  • Sven Banisch
  • Tanya Araujo
  • Jorge Louçã

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 as k-dimensional bitstrings. Individuals interact if the difference in the opinion strings is below a defined similarity threshold dI. Depending on dI, different behaviour 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 parameters dI and k, 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 analysing 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 non-trivial social structures emerge from simple rules for individual communication.

Suggested Citation

  • Sven Banisch & Tanya Araujo & Jorge Louçã, 2009. "Opinion Dynamics and Communication Networks," Working Papers Department of Economics 2009/16, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    • Handle: RePEc:ise:isegwp:wp162009
    as

    Download full text from publisher

    File URL: http://pascal.iseg.utl.pt/~depeco/wp/wp162009.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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, September.
    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. Shang, Lihui & Zhao, Mingming & Ai, Jun & Su, Zhan, 2021. "Opinion evolution in the Sznajd model on interdependent chains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    8. Floriana Gargiulo & José J Ramasco, 2012. "Influence of Opinion Dynamics on the Evolution of Games," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-7, November.
    9. 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.
    10. Yoo, Seung Han, 2014. "Learning a population distribution," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 188-201.
    11. 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.
    12. Moulet, Sonia & Rouchier, Juliette, 2008. "The influence of seller learning and time constraints on sequential bargaining in an artificial perishable goods market," Journal of Economic Dynamics and Control, Elsevier, vol. 32(7), pages 2322-2348, July.
    13. 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.
    14. He, Xue-Zhong & Lin, Shen, 2022. "Reinforcement Learning Equilibrium in Limit Order Markets," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    15. Serge Galam, 2016. "The invisible hand and the rational agent are behind bubbles and crashes," Papers 1601.02990, arXiv.org.
    16. Hang-Hyun Jo & Jeoung-Yoo Kim, 2012. "Competitive Targeted Marketing," ISER Discussion Paper 0834, Institute of Social and Economic Research, Osaka University.
    17. Gani Aldashev & Timoteo Carletti & Simone Righi, 2010. "Adaptive Expectations, Confirmatory Bias, and Informational Efficiency," Papers 1009.5075, arXiv.org.
    18. Michael Pickhardt, "undated". "A few can do – Ethical behavior and the provision of public goods in an agent-based model," Working Papers 201037, Institute of Spatial and Housing Economics, Munster Universitary.
    19. Huang, Changwei & Hou, Yongzhao & Han, Wenchen, 2023. "Coevolution of consensus and cooperation in evolutionary Hegselmann–Krause dilemma with the cooperation cost," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    20. María Cecilia Gimenez & Luis Reinaudi & Ana Pamela Paz-García & Paulo Marcelo Centres & Antonio José Ramirez-Pastor, 2021. "Opinion evolution in the presence of constant propaganda: homogeneous and localized cases," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(1), pages 1-11, January.

    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:ise:isegwp:wp162009. 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: Vitor Escaria (email available below). General contact details of provider: https://aquila.iseg.ulisboa.pt/aquila/departamentos/EC .

    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.