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Opinion Dynamics and Communication Networks

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  • 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
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    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.
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    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.
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    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.

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