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Technology and Culture: the Dissemination and the Potential 'Lock-In' of New Technologies

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Abstract

How do technological innovations change the patterns of their cultural diffusion in socio-economic networks? Cellular automata enable us to show Arthur's (1988) model of a potential 'lock-in' of a new technology in terms of dominant colours on the screen. The 'lock-in' effects can be combined with local learning, network effects, and more complex dynamics. Recursive and interaction terms can thus be declared separately in the construction of a simple, but non-linear model of technological development and innovation. This enables us to specify conditions for a 'break-out' or a 'deadlock' between competing technologies. Using Axelrod's (1997) simulation model of 'cultural dissemination' as another network effect, it will be shown that the cultural assimilation of a new technology can co-evolve with the 'lock-in' of a dominant technology. This effect can be annihilated by the further development of the communication with an emerging dimension. Implications for technology and innovation policies will be specified.

Suggested Citation

  • Loet Leydesdorff, 2001. "Technology and Culture: the Dissemination and the Potential 'Lock-In' of New Technologies," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 4(3), pages 1-5.
    • Handle: RePEc:jas:jasssj:2001-1-1
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    Cited by:

    1. Rossi, Federica, 2002. "An introductory overview of innovation studies," MPRA Paper 9106, University Library of Munich, Germany, revised Jun 2008.
    2. Kazuya Yamamoto, 2015. "Mobilization, Flexibility of Identity, and Ethnic Cleavage," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(2), pages 1-8.
    3. Juan Carlos González-Avella & Mario G. Cosenza & Konstantin Klemm & Víctor M. Eguíluz & Maxi San Miguel, 2007. "Information Feedback and Mass Media Effects in Cultural Dynamics," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(3), pages 1-9.
    4. Ma, Tieju & Nakamori, Yoshiteru, 2005. "Agent-based modeling on technological innovation as an evolutionary process," European Journal of Operational Research, Elsevier, vol. 166(3), pages 741-755, November.
    5. Luis R. Izquierdo & Segismundo S. Izquierdo & José Manuel Galán & José Ignacio Santos, 2009. "Techniques to Understand Computer Simulations: Markov Chain Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-6.
    6. Marc R.H. Roedenbeck & Barnas Nothnagel, 2007. "Rethinking Lock-in and Locking: Adopters Facing Network Effects," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(1), pages 1-4.
    7. Hung, Shih-Chang & Tu, Min-Fen, 2014. "Is small actually big? The chaos of technological change," Research Policy, Elsevier, vol. 43(7), pages 1227-1238.
    8. Yaofeng Zhang & Renbin Xiao, 2015. "Modeling and Simulation of Polarization in Internet Group Opinions Based on Cellular Automata," Discrete Dynamics in Nature and Society, Hindawi, vol. 2015, pages 1-15, August.
    9. Loet Leydesdorff, 2015. "Can intellectual processes in the sciences also be simulated? The anticipation and visualization of possible future states," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2197-2214, December.

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