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How Realistic Should Knowledge Diffusion Models Be?



Knowledge diffusion models typically involve two main features: an underlying social network topology on one side, and a particular design of interaction rules driving knowledge transmission on the other side. Acknowledging the need for realistic topologies and adoption behaviors backed by empirical measurements, it becomes unclear how accurately existing models render real-world phenomena: if indeed both topology and transmission mechanisms have a key impact on these phenomena, to which extent does the use of more or less stylized assumptions affect modeling results? In order to evaluate various classical topologies and mechanisms, we push the comparison to more empirical benchmarks: real-world network structures and empirically measured mechanisms. Special attention is paid to appraising the discrepancy between diffusion phenomena (i) on some real network topologies vs. various kinds of scale-free networks, and (ii) using an empirically-measured transmission mechanism, compared with canonical appropriate models such as threshold models. We find very sensible differences between the more realistic settings and their traditional stylized counterparts. On the whole, our point is thus also epistemological by insisting that models should be tested against simulation-based empirical benchmarks.

Suggested Citation

  • Jean-Philippe Cointet & Camille Roth, 2007. "How Realistic Should Knowledge Diffusion Models Be?," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(3), pages 1-5.
  • Handle: RePEc:jas:jasssj:2006-69-2

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    2. Pedro Campos & Pavel Brazdil & Isabel Mota, 2013. "Comparing Strategies of Collaborative Networks for R&D: An Agent-Based Study," Computational Economics, Springer;Society for Computational Economics, vol. 42(1), pages 1-22, June.
    3. Meysam Alizadeh & Claudio Cioffi-Revilla & Andrew Crooks, 0. "Generating and analyzing spatial social networks," Computational and Mathematical Organization Theory, Springer, vol. 0, pages 1-29.
    4. Jeehong Kim & Wonchang Hur, 2013. "Diffusion of competing innovations in influence networks," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 109-124, April.


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