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Heterogeneity vs. externalities in technological competition: A tale of possible technological landscapes

Author

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  • Jean-Michel Dalle

    (Ecole Polytechnique & IEPE-CNRS, 59-61, rue Pouchet, F-75017 Paris, France)

Abstract

The article presents a stochastic interaction model based on Gibbs random fields to analyze technological competition in a population of heterogeneous adopters with local or global externalities. The relationships between both heterogeneity and externalities and imperfect and asymmetric information are first emphasized. When local externalities and heterogeneity coexist, the technological landscapes of the industry are then shown to depend on the relative influence of these two parameters, with a phase transition: technologies coexist either in approximately equal market shares when heterogeneity is high enough or with one of the technologies only surviving in technological niches when local externalities dominate. Niches do also spontaneously appear: technological options survive in economic space due to the existence of some amount of heterogeneity among agents. On the contrary, when global externalities are added, pure standardization almost always occurs. We finally argue that different public policies should be designed so as to fit with different technological landscapes.

Suggested Citation

  • Jean-Michel Dalle, 1997. "Heterogeneity vs. externalities in technological competition: A tale of possible technological landscapes," Journal of Evolutionary Economics, Springer, vol. 7(4), pages 395-413.
    • Handle: RePEc:spr:joevec:v:7:y:1997:i:4:p:395-413
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    Citations

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    Cited by:

    1. Dalle, Jean-Michel & Jullien, Nicolas, 2003. "'Libre' software: turning fads into institutions?," Research Policy, Elsevier, vol. 32(1), pages 1-11, January.
    2. Daron Acemoglu, 2011. "Diversity and Technological Progress," NBER Chapters, in: The Rate and Direction of Inventive Activity Revisited, pages 319-356, National Bureau of Economic Research, Inc.
    3. Huotari, Pontus & Järvi, Kati & Kortelainen, Samuli & Huhtamäki, Jukka, 2017. "Winner does not take all: Selective attention and local bias in platform-based markets," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 313-326.
    4. 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.
    5. Jean-Michel Dalle & Paul A. David, 2007. "“It Takes All Kinds”: A Simulation Modeling Perspective on Motivation and Coordination in Libre Software Development Projects," Discussion Papers 07-024, Stanford Institute for Economic Policy Research.
    6. Jean-Michel Dalle & Paul A. David, 2005. "Simulating Code Growth in Libre (Open-Source) Mode," Discussion Papers 04-002, Stanford Institute for Economic Policy Research.
    7. 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.
    8. 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.
    9. Christian Garavaglia & Franco Malerba & Luigi Orsenigo & Michele Pezzoni, 2013. "Technological Regimes and Demand Structure in the Evolution of the Pharmaceutical Industry," Economic Complexity and Evolution, in: Andreas Pyka & Esben Sloth Andersen (ed.), Long Term Economic Development, edition 127, pages 61-94, Springer.
    10. Jean-Michel Dalle & Paul A. David, 2005. "SimCode: Agent-based Simulation Modelling of Open-Source Software Development," Industrial Organization 0502008, University Library of Munich, Germany.
    11. Steinhilber, Simone & Wells, Peter & Thankappan, Samarthia, 2013. "Socio-technical inertia: Understanding the barriers to electric vehicles," Energy Policy, Elsevier, vol. 60(C), pages 531-539.
    12. Pigeard de Almeida Prado, Fernando & Belitsky, Vladimir & Ferreira, Alex Luiz, 2011. "Social interactions, product differentiation and discontinuity of demand," Journal of Mathematical Economics, Elsevier, vol. 47(4-5), pages 642-653.
    13. Deroian, Frederic, 2002. "Formation of social networks and diffusion of innovations," Research Policy, Elsevier, vol. 31(5), pages 835-846, July.
    14. Koen Frenken, 2006. "Technological innovation and complexity theory," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 15(2), pages 137-155.
    15. Christian Garavaglia & Franco Malerba & Luigi Orsenigo & Michele Pezzoni, 2010. "A History-Friendly Model of the Evolution of the Pharmaceutical Industry: Technological Regimes and Demand Structure," KITeS Working Papers 036, KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy, revised Nov 2010.
    16. Faber, Albert & Hoppe, Thomas, 2013. "Co-constructing a sustainable built environment in the Netherlands—Dynamics and opportunities in an environmental sectoral innovation system," Energy Policy, Elsevier, vol. 52(C), pages 628-638.
    17. Andreas Reinstaller, 2013. "An Evolutionary View on Social Innovation and the Process of Economic Change. WWWforEurope Working Paper No. 43," WIFO Studies, WIFO, number 47018, April.
    18. Sun Hi Yoo & DongKyu Won, 2018. "Simulation of Weak Signals of Nanotechnology Innovation in Complex System," Sustainability, MDPI, vol. 10(2), pages 1-14, February.
    19. Frenken, Koen, 2000. "A complexity approach to innovation networks. The case of the aircraft industry (1909-1997)," Research Policy, Elsevier, vol. 29(2), pages 257-272, February.

    More about this item

    Keywords

    Externalities ; Heterogeneity ; Local interactions ; Global interactions ; Phase transition ; Standardization ; Stochastic models ; Technological competition ; Technological niches ; Economic landscapes;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General

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