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Modelling of Excitation Propagation for Social Interactions

  • Darius Plikynas
  • Aistis Raudys
  • Šarūnas Raudys
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8531)

Abstract

This paper investigates regularities in excitation information propagation in social interaction. We use cellular automaton approach where it is assumed that social media is composed from tens of thousands of community agents. Each agent can transmit and get a signal from several nearest neighbours. Weighted sums of input signals after reaction delay are transmitted to the closest agents. The model’s originality consists in the exploitation of neuron-based agent schema with nonlinear activation function employed to determine the reaction delay, the agent recovery period, and algorithms that define cooperation of several excitable groups. In the grouped model, each agent group can send its excitation signal to other groups. The agents and their groups should acquire diverse media parameters of social media in order to ensure desirable for social media character of excitation wave propagation patterns. The novel media model allows methodical analysis of propagation of several competing novelty signals. Simulations are very fast and can be useful for understanding and control of the simulated human and agent-based social mediums, planning and performing social and economy research.

Keywords

agents based modelling social medium excitation waves grouped populations propagation of novelties 

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References

  1. 1.
    Acemoglu, D., Ozdaglar, A., Yildiz, E.: Diffusion of innovations in social networks, Technical Report. Massachusets Institute of Technology (2012)Google Scholar
  2. 2.
    Alkemade, F., Castaldi, C.: Strategies for the diffusion of innovations on social networks. Computational Economics 25, 3–23 (2005)CrossRefzbMATHGoogle Scholar
  3. 3.
    Bandini, S., Federici, M.L., Vizzari, G.: Situated cellular agents approach to crowd modelling and simulation. Cybern. and Syst: Int. J. 38(7), 729–753 (2007)CrossRefzbMATHGoogle Scholar
  4. 4.
    Ben-Jackob, I.E., Cohen, I.: Cooperative self-organization of microorganisms. Advances in Physics 49(4), 395–554 (2000)CrossRefGoogle Scholar
  5. 5.
    Berestycki, H., Rodriguez, N., Ryzhik, L.: Traveling wave solutions in a reaction-diffusion model for criminal activity (2013), http://arxiv.org/ftp/arxiv/papers/1302/1302.4333.pdf
  6. 6.
    Chua, L.O., Hasler, M., Moschyt, G.S., Neirynck, J.: Autonomous cellular neural networks: a unified paradigm for pattern formation and active wave propagation. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 42(10), 559–577 (1995)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Cruz, C., González, J.R., Pelta, D.: Optimization in dynamic environments: a survey on problems, methods and measures. Soft Computing - A Fusion of Foundations, Methodologies and Applications 15, 1427–1448 (2011)Google Scholar
  8. 8.
    Haykin, S.: Neural Networks: A comprehensive foundation, 2nd edn. Macmillan College Publishing Company, New York (1998)Google Scholar
  9. 9.
    Helbing, D., Grund, T.U.: Editorial: agent-based modelling and techno-social systems. Advances in Complex Systems 16(04n05), 1303002 (2013)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Keller-Schmidt, S., Klemm, K.: A model of macro-evolution as a branching process based on innovations. Advances in Complex Systems 15(7), 1250043 (2012)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Litvak-Hinenzon, A., Stone, L.: Epidemic waves, small worlds and targeted vaccination, Biomathematics Unit, Faculty of Life Sciences, Tel Aviv University (2013)Google Scholar
  12. 12.
    Martin, P.M.V., Granel, E.M.: 2,500-year Evolution of the term epidemic. Emerging Infectious Diseases 12(6), 976–980 (2006)CrossRefGoogle Scholar
  13. 13.
    Moe, G.K., Rheinbolt, W.C., Abildskov, J.: A computer model of atrial fibrillation. Am. Heart J. 67, 200–220 (1964)CrossRefGoogle Scholar
  14. 14.
    De Paoli, F., Vizzari, G.: Context dependent management of field diffusion: an experimental framework. In: Proc. WOA (Pitagora Editrice Bologna 2003), pp. 78–84 (2003)Google Scholar
  15. 15.
    Plikynas, D.: A virtual field-based conceptual framework for the simulation of complex social systems. Journal of Systems Science and Complexity 23(2), 232–248 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Raudys, S.: Statistical and Neural Classifiers: An integrated approach to design. Springer (2001)Google Scholar
  17. 17.
    Raudys, S.: On the universality of the single-layer perceptron model. In: Neural Networks and Soft Computing. AISC, vol. 19, pp. 79–86. Springer, Wien (2003)CrossRefGoogle Scholar
  18. 18.
    Raudys, S.: Information transmission concept based model of wave propagation in discrete excitable media. Nonlinear Analysis: Modelling and Control 9(3), 271–289 (2004)zbMATHMathSciNetGoogle Scholar
  19. 19.
    Raudys, S.: A target value control while training the perceptrons in changing environments. In: Guo, M., Zhao, L., Wang, L. (eds.) Proc. of the 4th Int.l Conf. on Natural Computation, pp. 54–58. IEEE Computer Society Press (2008)Google Scholar
  20. 20.
    Raudys, S., Raudys, A.: Three decision making levels in portfolio management. In: Proc. IEEE Conf. Computational Intelligence for Financial Engineering and Economics, pp. 197–204. IEEE Computer Society Press, NYC (2012)Google Scholar
  21. 21.
    Raudys, S.: Portfolio of automated trading systems: Complexity and learning set size issues. IEEE Transacions on Neural Networks and Learning Systems 24(3), 448–459 (2013)CrossRefGoogle Scholar
  22. 22.
    Reber, A.S.: Implicit learning and tacit knowledge: an essay on the cognitive unconscious. Oxford University Press (1996)Google Scholar
  23. 23.
    Spach, M.S.: Discontinuous cardiac conduction: its origin in cellular connectivity with long-term adaptive changes that cause arrhytmias. In: Spooner, P.M., Joynes, R.W., Jalife, J. (eds.) Discontinuous Conduction in the Heart, pp. 5–51. Futura Publ. Company, Inc, Armonk (1997)Google Scholar
  24. 24.
    Steele, A.J., Tinsley, M., Showalter, K.: Collective behaviour of stabilized reaction-diffusion waves. Chaos 18, 026108 (2008)Google Scholar
  25. 25.
    Yde, P., Jensen, H., Trusina, A.: Analyzing inflammatory response as excitable media. Physical Review E 84, 051913 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Darius Plikynas
    • 1
  • Aistis Raudys
    • 1
  • Šarūnas Raudys
    • 2
  1. 1.Research and Development CenterKazimieras Simonavicius UniversityVilniusLithuania
  2. 2.Faculty of Mathematics and InformaticsVilnius UniversityVilniusLithuania

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