Abstract
Social networks provide a natural infrastructure for knowledge creation and exchange. In this paper, we study the effects of a skewed degree distribution within formal networks on knowledge exchange and diffusion processes. To investigate how the structure of networks affects diffusion performance, we use an agent-based simulation model of four theoretical networks as well as an empirical network. Our results indicate an interesting effect: neither path length nor clustering coefficient is the decisive factor determining diffusion performance but the skewness of the link distribution is. Building on the concept of cognitive distance, our model shows that even in networks where knowledge can diffuse freely, poorly connected nodes are excluded from joint learning in networks.
Original language | English |
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Pages (from-to) | 388-407 |
Number of pages | 20 |
Journal | International Journal of Computational Economics and Econometrics |
Volume | 8 |
Issue number | 3-4 |
DOIs | |
Publication status | Published - Nov 2018 |
Externally published | Yes |
Keywords
- agent-based simulation
- cognitive distance
- degree distribution
- direct project funding
- foerderkatalog
- german energy sector
- innovation networks
- knowledge diffusion
- publicly funded R&D projects
- random networks
- scale-free networks
- simulation of empirical networks
- skewness
- Small-World Network