IDEAS home Printed from https://ideas.repec.org/p/uto/dipeco/201205.html
   My bibliography  Save this paper

A percolation model of multi-technology diffusion: information feedbacks, learning economies and subsidy policy

Author

Listed:

Abstract

This paper deals with the diffusion of innovations in a multi-technology setting. High up-front costs of adoption, heterogeneity among potential adopters, network interactions, information feedbacks and subsidy policies are reproduced by an agent-based percolation model of multi-technology diffusion. In our model a new technology incorporated in a final product ready to be commercialized may spread in a market of heterogeneous consumers who decide whether to adopt it or not depending on both the net benefit from adoption and on locally available information. A new desirable technology, characterized by a high up-front cost of adoption, may not be able to overcome the obstacles to its diffusion despite potential future cost reductions. It may fail to spread in the market because of the pressure from its competitors (i.e. other technologies that serve a similar function) or because heterogeneity among potential adopters confine the spread of useful information to isolated sub-communities. We ask if a subsidy policy would trigger a self-sustained diffusion of a desirable technology. We run the model in two specific network topologies: bidimensional regular lattice and small world network. We show that a) information feedbacks and learning economies give raise to a positive feedback loop almost independently on the topology of the network: more information feedbacks ® decrease in price ® higher probability of conquering potential adopters ® more information feedbacks etc; b) market dominance depends on the probability of the initial adopters to belong to an expanding cluster which is a function of both the network topology and heterogeneity of potential adopters; c) in a multi-technology setting a subsidy policy should be set not only according to future costs reduction and heterogeneity but also to competition and technologies interdependence: reaching the necessary critical mass of diffusion may depend on how successfully the overall spread of other undesirable technologies is prevented

Suggested Citation

  • Cantono Simona, 2012. "A percolation model of multi-technology diffusion: information feedbacks, learning economies and subsidy policy," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201205, University of Turin.
    • Handle: RePEc:uto:dipeco:201205
    as

    Download full text from publisher

    File URL: http://www.est.unito.it/do/home.pl/Download?doc=/allegati/wp2012dip/5_wp_cantono.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Cristiano Antonelli, 1996. "Localized knowledge percolation processes and information networks," Journal of Evolutionary Economics, Springer, vol. 6(3), pages 281-295.
    3. 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.
    4. 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).
    5. Simona Cantono & Gerald Silverberg, 2008. "A percolation model of eco-innovation diffusion: the relationship between diffusion, learning economies and subsidies," MERIT Working Papers 2008-025, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    6. 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.
    7. Davies, Stephen W., 1979. "Inter-firm diffusion of process innovations," European Economic Review, Elsevier, vol. 12(4), pages 299-317, October.
    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. 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. 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.
    2. Paolo Zeppini & Koen Frenken, 2015. "Networks, Percolation, and Demand," Department of Economics Working Papers 38/15, University of Bath, Department of Economics.
    3. Albert Faber & Koen Frenken, 2008. "Models in evolutionary economics and environmental policy: Towards an evolutionary environmental economics," Innovation Studies Utrecht (ISU) working paper series 08-15, Utrecht University, Department of Innovation Studies, revised Apr 2008.
    4. Vona, Francesco & Patriarca, Fabrizio, 2011. "Income inequality and the development of environmental technologies," Ecological Economics, Elsevier, vol. 70(11), pages 2201-2213, September.
    5. Karolina Safarzyńska & Jeroen Bergh, 2010. "Evolutionary models in economics: a survey of methods and building blocks," Journal of Evolutionary Economics, Springer, vol. 20(3), pages 329-373, June.
    6. Paolo Zeppini & Koen Frenken & Roland Kupers, 2013. "Threshold models of technological transitions," Working Papers 13-06, Eindhoven Center for Innovation Studies, revised Aug 2013.
    7. 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.
    8. Kanai, Yasuhiro & Abe, Keiji & Seki, Yoichi, 2015. "Price percolation model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 226-233.
    9. D’Orazio, Paola & Valente, Marco, 2019. "The role of finance in environmental innovation diffusion: An evolutionary modeling approach," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 417-439.
    10. Alessandro Caiani, 2017. "Innovation Dynamics and Industry Structure Under Different Technological Spaces," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 3(3), pages 307-341, November.
    11. Thomas Grebel, 2013. "Network Evolution in Basic Science," Economic Complexity and Evolution, in: Guido Buenstorf & Uwe Cantner & Horst Hanusch & Michael Hutter & Hans-Walter Lorenz & Fritz Rahmeyer (ed.), The Two Sides of Innovation, edition 127, pages 39-53, Springer.
    12. 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.
    13. Fibich, Gadi & Levin, Tomer, 2020. "Percolation of new products," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    14. Thomas Grebel, 2011. "Innovation and Health," Books, Edward Elgar Publishing, number 14375.
    15. 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.
    16. Rui Leite & Aurora Teixeira, 2012. "Innovation diffusion with heterogeneous networked agents: a computational model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 7(2), pages 125-144, October.
    17. Simona Cantono & Gerald Silverberg, 2008. "A percolation model of eco-innovation diffusion: the relationship between diffusion, learning economies and subsidies," MERIT Working Papers 2008-025, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    18. Jiang, Yonglei & Timmermans, Harry J.P. & Yu, Bin, 2018. "Relocation of manufacturing industry from the perspective of transport accessibility – An application of percolation theory," Transport Policy, Elsevier, vol. 63(C), pages 10-29.
    19. Kindler, A. & Solomon, S. & Stauffer, D., 2013. "Peer-to-peer and mass communication effect on opinion shifts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 785-796.
    20. Alessandro Caiani, 2012. "An Agent-Based Model of Schumpeterian Competition," Quaderni di Dipartimento 176, University of Pavia, Department of Economics and Quantitative Methods.

    More about this item

    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:uto:dipeco:201205. 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: Piero Cavaleri or Marina Grazioli (email available below). General contact details of provider: https://edirc.repec.org/data/detorit.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.