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Innovation diffusion with heterogeneous networked agents: a computational model

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  • Rui Leite
  • Aurora Teixeira

Abstract

It is well established that S-shaped curves describe the diffusion processes of many innovations quite well, but little insight on the mechanics of diffusion is achieved by simple curve fitting. We propose an evolutionary model of the diffusion process, focusing on the characteristics of economic agents and on the interactions among them, and relate those determinants with the observed shape of the diffusion curve. Using simulation techniques, we show that the proposed model is able to explain why an innovation may not diffuse globally across an economy/region, even when it faces no rival innovations. Moreover, we show how network size, informational spillovers, and the behavior of innovation prices shape the diffusion process. The results regarding network size and informational spillovers rationalize the importance of informational lock-outs, proving they can influence both the aggregate adoption rate and the speed of the diffusion process. With respect to innovation prices, simulation results show that faster price decline leads to higher aggregate adoption rates, and that the diffusion process is more sensitive to the pricing dynamics than to the network size or the behavior of spillovers. Copyright Springer-Verlag 2012

Suggested Citation

  • 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.
    • Handle: RePEc:spr:jeicoo:v:7:y:2012:i:2:p:125-144
    DOI: 10.1007/s11403-011-0086-x
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    References listed on IDEAS

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    Cited by:

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    2. Pedro Lopez-Merino & Juliette Rouchier, 2021. "An agent-based model of (food) consumption: Accounting for the Intention-Behaviour-Gap on three dimensions of characteristics with limited knowledge," Post-Print hal-03618377, HAL.
    3. Haibo Hu & Jonathan J. H. Zhu, 2017. "Social networks, mass media and public opinions," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(2), pages 393-411, July.
    4. Yanxin Liu & Huajiao Li & Jianhe Guan & Xueyong Liu & Yajie Qi, 2019. "The role of the world’s major steel markets in price spillover networks: an analysis based on complex network motifs," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(4), pages 697-720, December.

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    More about this item

    Keywords

    Innovation diffusion; Evolutionary agent-based models; Network effects; Knowledge spillovers; O33;
    All these keywords.

    JEL classification:

    • 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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