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Networks, Percolation, and Consumer Demand

Author

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  • Paolo Zeppini

    (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur)

  • Koen Frenken

    (Urban and Regional research centre Utrecht (URU) - Universiteit Utrecht / Utrecht University [Utrecht])

Abstract

Understanding diffusion processes is key to market strategies as well as innovation and sustainability policies. In promoting new products and technologies, firms and governments need to understand the conditions favouring successful spread of these products. We propose a generic diffiusion model based on percolation theory. Our reference is a new product diffusion in a social network through word-of-mouth. Given that consumers differ in their reservation prices, a critical price exists that defines a phase transition froma no-diffusion to a diffusion regime. As consumer surplus is maximised just below a product's critical price, one can systematically compare the economic efficiency of network structures by investigating their critical price. Networks with lowclustering were themost efficient, because clustering leads to redundant information flows hampering effective product diffusion. We further showed that the more equal a society, the more efficient the diffusion process.

Suggested Citation

  • Paolo Zeppini & Koen Frenken, 2018. "Networks, Percolation, and Consumer Demand," Post-Print halshs-01952450, HAL.
  • Handle: RePEc:hal:journl:halshs-01952450
    DOI: 10.18564/jasss.3658
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    References listed on IDEAS

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    1. Alessandra Fogli & Laura Veldkamp, 2021. "Germs, Social Networks, and Growth [Unbundling Institutions]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(3), pages 1074-1100.
    2. Chung-Yuan Huang & Chuen-Tsai Sun & Hsun-Cheng Lin, 2005. "Influence of Local Information on Social Simulations in Small-World Network Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-8.
    3. Yaniv Dover & Jacob Goldenberg & Daniel Shapira, 2012. "Network Traces on Penetration: Uncovering Degree Distribution from Adoption Data," Marketing Science, INFORMS, vol. 31(4), pages 689-712, July.
    4. Martin Hohnisch & Sabine Pittnauer & Dietrich Stauffer, 2008. "A percolation-based model explaining delayed takeoff in new-product diffusion," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 17(5), pages 1001-1017, October.
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    Cited by:

    1. Firouzeh Taghikhah & Tatiana Filatova & Alexey Voinov, 2021. "Where Does Theory Have It Right? A Comparison of Theory-Driven and Empirical Agent Based Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 24(2), pages 1-4.

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