IDEAS home Printed from https://ideas.repec.org/p/ein/tuecis/1302.html
   My bibliography  Save this paper

Innovation diffusion in networks: the microeconomics of percolation

Author

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
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    References listed on IDEAS

    as
    1. Alessandra Fogli & Laura Veldkamp, 2021. "Germs, Social Networks, and Growth [Unbundling Institutions]," Review of Economic Studies, Oxford University Press, vol. 88(3), pages 1074-1100.
    2. Blume Lawrence E., 1995. "The Statistical Mechanics of Best-Response Strategy Revision," Games and Economic Behavior, Elsevier, vol. 11(2), pages 111-145, November.
    3. Cristopher Moore & M. E. J. Newman, 2000. "Epidemics and Percolation in Small-World Networks," Working Papers 00-01-002, Santa Fe Institute.
    4. Vega-Redondo,Fernando, 2007. "Complex Social Networks," Cambridge Books, Cambridge University Press, number 9780521857406.
    5. Sanjeev Goyal, 2007. "Introduction to Connections: An Introduction to the Economics of Networks," Introductory Chapters, in: Connections: An Introduction to the Economics of Networks, Princeton University Press.
    6. Frenken, Koen & Silverberg, Gerald & Valente, Marco, 2008. "A percolation model of the product lifecycle," MERIT Working Papers 2008-073, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    7. Arthur, W Brian, 1989. "Competing Technologies, Increasing Returns, and Lock-In by Historical Events," Economic Journal, Royal Economic Society, vol. 99(394), pages 116-131, March.
    8. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    9. Katz, Michael L & Shapiro, Carl, 1986. "Technology Adoption in the Presence of Network Externalities," Journal of Political Economy, University of Chicago Press, vol. 94(4), pages 822-841, August.
    10. Banerjee, Abhijit & Jackson, Matthew O. & Duflo, Esther & Chandrasekhar, Arun G., 2012. "The Diffusion of Microfinance," CEPR Discussion Papers 8770, C.E.P.R. Discussion Papers.
    11. Argote, L. & Epple, D., 1990. "Learning Curves In Manufacturing," GSIA Working Papers 89-90-02, Carnegie Mellon University, Tepper School of Business.
    12. Solomon, Sorin & Weisbuch, Gerard & de Arcangelis, Lucilla & Jan, Naeem & Stauffer, Dietrich, 2000. "Social percolation models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 277(1), pages 239-247.
    13. Arthur Campbell, 2013. "Word-of-Mouth Communication and Percolation in Social Networks," American Economic Review, American Economic Association, vol. 103(6), pages 2466-2498, October.
    14. López-Pintado, Dunia, 2008. "Diffusion in complex social networks," Games and Economic Behavior, Elsevier, vol. 62(2), pages 573-590, March.
    15. S. Davis & P. Trapman & H. Leirs & M. Begon & J. A. P. Heesterbeek, 2008. "The abundance threshold for plague as a critical percolation phenomenon," Nature, Nature, vol. 454(7204), pages 634-637, July.
    16. Jean Tirole, 1988. "The Theory of Industrial Organization," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262200716, December.
    17. Silverberg, Gerald & Verspagen, Bart, 2005. "A percolation model of innovation in complex technology spaces," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 225-244, January.
    18. Vega-Redondo,Fernando, 2007. "Complex Social Networks," Cambridge Books, Cambridge University Press, number 9780521674096.
    19. 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.
    20. William A. Brock & Steven N. Durlauf, 2010. "Adoption Curves and Social Interactions," Journal of the European Economic Association, MIT Press, vol. 8(1), pages 232-251, March.
    21. A. Canepa & P. Stoneman, 2004. "Comparative international diffusion: Patterns, determinants and policies," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 13(3), pages 279-298.
    22. Eocman Lee & Jeho Lee & Jongseok Lee, 2006. "Reconsideration of the Winner-Take-All Hypothesis: Complex Networks and Local Bias," Management Science, INFORMS, vol. 52(12), pages 1838-1848, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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 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, December.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Paolo Zeppini & Koen Frenken & Roland Kupers, 2013. "The complexity of transitions," Working Papers 13-04, Eindhoven Center for Innovation Studies, revised Mar 2013.
    3. Cantono, Simona, 2012. "Unveiling diffusion dynamics: an autocatalytic percolation model of environmental innovation diffusion and the optimal dynamic path of adoption subsidies," Department of Economics and Statistics Cognetti de Martiis LEI & BRICK - Laboratory of Economics of Innovation "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio 201222, University of Turin.
    4. Jackson, Matthew O. & Zenou, Yves, 2015. "Games on Networks," Handbook of Game Theory with Economic Applications,, Elsevier.
    5. Paolo Zeppini & Koen Frenken, 2015. "Networks, Percolation, and Demand," Department of Economics Working Papers 38/15, University of Bath, Department of Economics.
    6. Oyama, Daisuke & Takahashi, Satoru, 2015. "Contagion and uninvadability in local interaction games: The bilingual game and general supermodular games," Journal of Economic Theory, Elsevier, vol. 157(C), pages 100-127.
    7. Nikolas Tsakas, 2014. "Optimal influence under observational learning," Gecomplexity Discussion Paper Series 4, Action IS1104 "The EU in the new complex geography of economic systems: models, tools and policy evaluation", revised Nov 2014.
    8. Paolo Zeppini & Koen Frenken & Roland Kupers, 2013. "Threshold models of technological transitions," Working Papers 13-06, Eindhoven Center for Innovation Studies, revised Aug 2013.
    9. Stavins, Robert & Jaffe, Adam & Newell, Richard, 2000. "Technological Change and the Environment," Working Paper Series rwp00-002, Harvard University, John F. Kennedy School of Government.
    10. Michael D. König & Xiaodong Liu & Yves Zenou, 2019. "R&D Networks: Theory, Empirics, and Policy Implications," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 476-491, July.
    11. Goyal, S., 2018. "Heterogeneity and Networks," Cambridge Working Papers in Economics 1812, Faculty of Economics, University of Cambridge.
    12. Wallace, Chris & Young, H. Peyton, 2015. "Stochastic Evolutionary Game Dynamics," Handbook of Game Theory with Economic Applications,, Elsevier.
    13. Matthew O. Jackson, 2014. "Networks in the Understanding of Economic Behaviors," Journal of Economic Perspectives, American Economic Association, vol. 28(4), pages 3-22, Fall.
    14. Oz Shy, 2011. "A Short Survey of Network Economics," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 38(2), pages 119-149, March.
    15. H Peyton Young & Lucas Merrill Brown, 2016. "The Diffusion of a Social Innovation: Executive Stock Options from 1936," Economics Series Working Papers 777, University of Oxford, Department of Economics.
    16. Pablo Branas-Garza & Natalia Jimenez & Giovanni Ponti, 2017. "Eliciting real-life social networks: a guided tour," Journal of Behavioral Economics for Policy, Society for the Advancement of Behavioral Economics (SABE), vol. 1(1), pages 33-39, February.
    17. Lelarge, Marc, 2012. "Diffusion and cascading behavior in random networks," Games and Economic Behavior, Elsevier, vol. 75(2), pages 752-775.
    18. Jaffe, Adam B. & Newell, Richard G. & Stavins, Robert N., 2003. "Chapter 11 Technological change and the environment," Handbook of Environmental Economics, in: K. G. Mäler & J. R. Vincent (ed.), Handbook of Environmental Economics, edition 1, volume 1, chapter 11, pages 461-516, Elsevier.
    19. Michel Grabisch & Agnieszka Rusinowska & Xavier Venel, 2019. "Diffusion in countably infinite networks," Documents de travail du Centre d'Economie de la Sorbonne 19017, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    20. Simon Weidenholzer, 2010. "Coordination Games and Local Interactions: A Survey of the Game Theoretic Literature," Games, MDPI, vol. 1(4), pages 1-35, November.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ein:tuecis:1302. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/ectuenl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.