It’s a match! Simulating compatibility-based learning in a network of networks

Publication Type

Journal contribution (peer reviewed)

Authors

Michael P. Schlaile, Johannes Zeman, Matthias Mueller

Year of publication

2018

Published in

Journal of Evolutionary Economics

DOI

10.1007/s00191-018-0579-z

Keywords

innovation, Innovationsakzeptanz, Lernen, Lernstrategien, Modellbildung, Netzwerke, internationale, simulation

In this article, we develop a new way to capture knowledge diffusion and assimilation in
innovation networks by means of an agent-based simulation model. The model
incorporates three essential characteristics of knowledge that have not been covered
entirely by previous diffusion models: the network character of knowledge, compatibility
of new knowledge with already existing knowledge, and the fact that transmission of
knowledge requires some form of attention. We employ a network-of-networks
approach, where agents are located within an innovation network and each agent itself
contains another network composed of knowledge units (KUs). Since social learning is
a path-dependent process, in our model, KUs are exchanged among agents and
integrated into their respective knowledge networks depending on the received KUs'
compatibility with the currently focused ones. Thereby, we are also able to endogenize
attributes such as absorptive capacity that have been treated as an exogenous
parameter in some of the previous diffusion models. We use our model to simulate and
analyze various scenarios, including cases for different degrees of knowledge diversity
and cognitive distance among agents as well as knowledge exploitation vs. exploration
strategies. Here, the model is able to distinguish between two levels of knowledge
diversity: heterogeneity within and between agents. Additionally, our simulation results
give fresh impetus to debates about the interplay of innovation network structure and
knowledge diffusion. In summary, our article proposes a novel way of modeling
knowledge diffusion, thereby contributing to an advancement of the economics of
innovation and knowledge.