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Innovation diffusion in networks: the microeconomics of percolation

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Listed:
  • Paolo Zeppini
  • Koen Frenken
  • Luis R. Izquierdo

Abstract

We implement a diffusion model for an innovative product in a market with a structure of social relationships. Diffusion is described with a percolation approach in the price space. Percolation shows a phase transition from a diffusion to a no-diffusion regime. This has strong implications for market demand and pricing. We study the effect of network structure on market diffusion efficiency by considering a number of cases, such as one-dimensional and two-dimensional lattices, small worlds, Poisson networks and Scale-free networks. We consider two measures of diffusion efficiency: the size of diffusion and the diffusion time-length. We find that network connectivity “spreading” is the most important factor for the size of diffusion. Clustering is ineffective. This means that societies with higher dimensionality are better markets for diffusion. This result is most evident for the size of diffusion, while a short average path-length is more important for the speed of diffusion. Endogenous learning curves shift the percolation threshold to higher prices, and constitute an endogenous mechanism of price discrimination. The best market strategy of innovation diffusion is to start with high price and allow for a learning curve.

Suggested Citation

  • Paolo Zeppini & Koen Frenken & Luis R. Izquierdo, 2013. "Innovation diffusion in networks: the microeconomics of percolation," Working Papers 13-02, Eindhoven Center for Innovation Studies, revised Feb 2013.
    • Handle: RePEc:ein:tuecis:1302
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    Cited by:

    1. Katarzyna Maciejowska & Arkadiusz Jedrzejewski & Anna Kowalska-Pyzalska & Katarzyna Sznajd-Weron & Rafal Weron, 2015. "Two faces of word-of-mouth: Understanding the impact of social interactions on demand curves for innovative products," HSC Research Reports HSC/15/09, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    2. Nan Lu, 2018. "La modélisation de l’indice CAC 40 avec un modèle basé agent," Erudite Ph.D Dissertations, Erudite, number ph18-02 edited by François Legendre.
    3. Bogner, Kristina, 2015. "The effect of project funding on innovative performance: An agent-based simulation model," Hohenheim Discussion Papers in Business, Economics and Social Sciences 10-2015, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    4. Paolo Zeppini & Koen Frenken & Roland Kupers, 2013. "Threshold models of technological transitions," Working Papers 13-06, Eindhoven Center for Innovation Studies, revised Aug 2013.
    5. Solomon Sorin & Golo Natasa, 2013. "Minsky Financial Instability, Interscale Feedback, Percolation and Marshall–Walras Disequilibrium," Accounting, Economics, and Law: A Convivium, De Gruyter, vol. 3(3), pages 167-260, October.

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    More about this item

    Keywords

    critical transition; demand; learning curves; market efficiency; social networks;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D42 - Microeconomics - - Market Structure, Pricing, and Design - - - Monopoly
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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