Agent-Based Spatial Dynamic Modeling of Opinion Propagation Exploring Delaying Conditions to Achieve Homogeneity

  • Leire OzaetaEmail author
  • Manuel Graña
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 527)


Most computational models of influence spread nowadays are motivated by the need to identify the social actors with maximal influence, in order to achieve high penetration in the market with minimal effort. However, there are little literature on the mechanisms of influence propagation, i.e. computational models of how the social actors change their opinions. There are some works that relate the spatial distribution of the opinions with the mechanism by which an agent changes or maintains its opinions, but they assume a cell model, where agents have fixed spatial locations and neighbors. Here we explore the effect of spatial interaction of the agents, which are free to move in a given space, following attraction dynamics towards agents with similar opinions. The spatial distribution of opinions observed by the agent is used by the agent to decide about opinion changes. We report preliminary results of simulations carried out in Netlogo environment for the first three kinds of systems.


Compact Group Neighbor Selection Initial Opinion Opinion Number Influence Spread 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Leire Ozaeta has been supported by a Predoctoral grant from the Basque Government.


  1. 1.
    Banisch, S., Lima, R., Araujo, T.: Agent based model and opinion dynamics as markov chains. Soc. Netw. 34, 549–561 (2012)CrossRefGoogle Scholar
  2. 2.
    Crokidakis, N.: Effects of mass media on opinion spreading in the sznajd sociophysics model. Phys. A Stat. Mech. Appl. 391, 1729–1734 (2012)CrossRefGoogle Scholar
  3. 3.
    Deffuant, G., Amblard, F., Weisbuch, G., Faure, T.: How can extremism prevail? A study based on the relative agreement interaction model. J. Artif. Soc. Soc. Simul. 5(2) (2002)Google Scholar
  4. 4.
    Gil, S., Zanette, D.H.: Coevolution of agent and networks: opinion spreading and community disconnection. Phys. Lett. A 356, 89–94 (2006)CrossRefzbMATHGoogle Scholar
  5. 5.
    Dreyer Jr., P.A., Roberts, F.S.: Irreversible k-threshold processes: graph-theorical threshold models of the spread of disease and of opinion. Discrete Appl. Math. 157, 1615–1627 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Jung, J., Bramson, A.: An agent - based model of indirect minority influence on social change. In: ALIFE 14 (2014)Google Scholar
  7. 7.
    Louie, M.A., Carley, K.M.: The role of dynamic-network multi-agent models of socio-political systems in policy. Technical report, CASOS (2007)Google Scholar
  8. 8.
    Mucchi-Faina, A., Paclilli, M.G., Pagliaro, S.: Minority influence, social change and social stability. Soc. Pers. Psychol. Compass 4, 1111–1123 (2010)CrossRefGoogle Scholar
  9. 9.
    Rouly, O.C.: At the root of sociality: working towards emergent, permanent, social affines. In: Proceedings of The European Conference on Artificial Life, pp. 82–89 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computational Intelligence Group, Department of CCIAUniversity of the Basque CountryLeioaSpain

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