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Influence of a network structure on the network effect in the communication service market

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  • Uchida, Makoto
  • Shirayama, Susumu

Abstract

In this study, we analyze the network effect in a model of a personal communication market, by using a multi-agent based simulation approach. We introduce into the simulation model complex network structures as the interaction patterns of agents. With complex network models, we investigate the dynamics of a market in which two providers are competing. We also examine the structure of networks that affect the complex behavior of the market. By a series of simulations, we show that the structural properties of complex networks, such as the clustering coefficient and degree correlation, have a major influence on the dynamics of the market. We find that the network effect is increased if the interaction pattern of agents is characterized by a high clustering coefficient, or a positive degree correlation. We also discuss a suitable model of the interaction pattern for reproducing market dynamics in the real world, by performing simulations using real data of a social network.

Suggested Citation

  • Uchida, Makoto & Shirayama, Susumu, 2008. "Influence of a network structure on the network effect in the communication service market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5303-5310.
    • Handle: RePEc:eee:phsmap:v:387:y:2008:i:21:p:5303-5310
    DOI: 10.1016/j.physa.2008.06.012
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    References listed on IDEAS

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    Cited by:

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    2. Torsten Heinrich, 2018. "Network Externalities and Compatibility Among Standards: A Replicator Dynamics and Simulation Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 52(3), pages 809-837, October.
    3. Muller, Eitan & Peres, Renana, 2019. "The effect of social networks structure on innovation performance: A review and directions for research," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 3-19.
    4. Geng, Sunyue & Liu, Sifeng & Fang, Zhigeng, 2022. "An agent-based algorithm for dynamic routing in service networks," European Journal of Operational Research, Elsevier, vol. 303(2), pages 719-734.
    5. Tatsuhiro Shichijo & Emiko Fukuda, 2019. "A dynamic game analysis of Internet services with network externalities," Theory and Decision, Springer, vol. 86(3), pages 361-388, May.
    6. Peres, Renana, 2014. "The impact of network characteristics on the diffusion of innovations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 330-343.
    7. Jeehong Kim & Wonchang Hur, 2013. "Diffusion of competing innovations in influence networks," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 109-124, April.
    8. Giovanni Pegoretti & Francesco Rentocchini & Giuseppe Vittucci Marzetti, 2012. "An agent-based model of innovation diffusion: network structure and coexistence under different information regimes," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 7(2), pages 145-165, October.
    9. Heinrich, Torsten, 2015. "A Replicator Dynamic and Simulation Analysis of Network Externalities and Compatibility Among Standards," MPRA Paper 67198, University Library of Munich, Germany.
    10. Gräbner, Claudius & Heinrich, Torsten & Kudic, Muhamed, 2016. "Structuration processes in complex dynamic systems - an overview and reassessment," MPRA Paper 69095, University Library of Munich, Germany.
    11. Guseo, Renato & Guidolin, Mariangela, 2010. "Cellular Automata with network incubation in information technology diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(12), pages 2422-2433.
    12. Heinrich, Torsten, 2016. "The Narrow and the Broad Approach to Evolutionary Modeling in Economics," MPRA Paper 75797, University Library of Munich, Germany.

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