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Competing for Customers in a Social Network

Author

Listed:
  • Pradeep K. Dubey

    (SBU - Stony Brook University [SUNY] - SUNY - State University of New York)

  • Rahul Garg

    (IBM India Research Laboratory - IBM)

  • Bernard de Meyer

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

Abstract

There are many situations in which a customer's proclivity to buy the product of any firm depends not only on the classical attributes of the product such as its price and quality, but also on who else is buying the same product. We model these situations as games in which firms compete for customers located in a "social network". Nash Equilibrium (NE) in pure strategies exist in general. In the quasi-linear version of the model, NE turn out to be unique and can be precisely characterized. If there are no a priori biases between customers and firms, then there is a cut-off level above which high cost firms are blockaded at an NE, while the rest compete uniformly throughout the network. We also explore the relation between the connectivity of a customer and the money firms spend on him. This relation becomes particularly transparent when externalities are dominant: NE can be characterized in terms of the invariant measures on the recurrent classes of the Markov chain underlying the social network. Finally we consider convex (instead of linear) cost functions for the firms. Here NE need not be unique as we show via an example. But uniqueness is restored if there is enough competition between firms or if their valuations of clients are anonymous.

Suggested Citation

  • Pradeep K. Dubey & Rahul Garg & Bernard de Meyer, 2006. "Competing for Customers in a Social Network," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00272889, HAL.
  • Handle: RePEc:hal:cesptp:hal-00272889
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    Citations

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    Cited by:

    1. Nicole Tabasso, 2014. "Diffusion of Multiple Information," School of Economics Discussion Papers 0914, School of Economics, University of Surrey.
    2. Antoine Mandel & Xavier Venel, 2017. "Dynamic competition over social networks Dynamic competition over social networks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01524453, HAL.
    3. Kolumbus, Yoav & Solomon, Sorin, 2021. "On the influence maximization problem and the percolation phase transition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    4. Rusinowska, Agnieszka & Taalaibekova, Akylai, 2019. "Opinion formation and targeting when persuaders have extreme and centrist opinions," Journal of Mathematical Economics, Elsevier, vol. 84(C), pages 9-27.
    5. Pradeep Dubey & Rahul Garg & Bernard De Meyer, 2006. "Competing for Customers in a Social Network," Cowles Foundation Discussion Papers 1591, Cowles Foundation for Research in Economics, Yale University.
    6. Mandel, Antoine & Venel, Xavier, 2020. "Dynamic competition over social networks," European Journal of Operational Research, Elsevier, vol. 280(2), pages 597-608.
    7. Sengupta, Abhijit & Greetham, Danica Vukadinovic, 2010. "Dynamics of brand competition: Effects of unobserved social networks," Journal of Economic Dynamics and Control, Elsevier, vol. 34(12), pages 2391-2406, December.
    8. Goyal, Sanjeev & Heidari, Hoda & Kearns, Michael, 2019. "Competitive contagion in networks," Games and Economic Behavior, Elsevier, vol. 113(C), pages 58-79.
    9. Antoine Mandel & Xavier Venel, 2017. "Dynamic competition over social networks Dynamic competition over social networks," Post-Print halshs-01524453, HAL.
    10. Tabasso, Nicole, 2019. "Diffusion of multiple information: On information resilience and the power of segregation," Games and Economic Behavior, Elsevier, vol. 118(C), pages 219-240.
    11. Aiche, A. & Einy, Ezra & Haimanko, Ori & Moreno, Diego & Selay, A. & Shitovitz, Benyamin, 2016. "Information advantage in common-value classic Tullock contests," UC3M Working papers. Economics 23939, Universidad Carlos III de Madrid. Departamento de Economía.
    12. Li, WenYao & Xue, Xiaoyu & Pan, Liming & Lin, Tao & Wang, Wei, 2022. "Competing spreading dynamics in simplicial complex," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    13. Jie Gu & Yunjie Xu, 2022. "Battle of positioning: exploring the role of bridges in competitive diffusion," Journal of Computational Social Science, Springer, vol. 5(1), pages 319-350, May.
    14. Demange, Gabrielle, 2017. "Optimal targeting strategies in a network under complementarities," Games and Economic Behavior, Elsevier, vol. 105(C), pages 84-103.
    15. Michel Grabisch & Antoine Mandel & Agnieszka Rusinowska & Emily Tanimura, 2018. "Strategic Influence in Social Networks," Mathematics of Operations Research, INFORMS, vol. 43(1), pages 29-50, February.
    16. Fairouz Medjahed & Elisenda Molina & Juan Tejada, 2025. "Effectiveness of Centrality Measures for Competitive Influence Diffusion in Social Networks," Mathematics, MDPI, vol. 13(2), pages 1-32, January.
    17. Akylai Taalaibekova, 2018. "Opinion formation in social networks," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 28(2), pages 85-108.

    More about this item

    Keywords

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    JEL classification:

    • A14 - General Economics and Teaching - - General Economics - - - Sociology of Economics
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • L2 - Industrial Organization - - Firm Objectives, Organization, and Behavior

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