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Network structure and optimal technological innovation

The role of networks in the emergence, diffusion and evolution of technological innovations has attracted much theoretical and empirical attention. Yet, much of the work has explored the role of undirected and homogeneous networks. In real cases, many networks are directed. The flow of information, benefits or observations is directed from one node towards another node. Real networks are also heterogeneous, for example, few nodes have a high degree while many others have a low degree.In this article, we report on the results of an evolutionary agent-based model in which a group of agents, in our case firms, collectively search a complex (rugged) technological landscape and observe each other’s solutions with different frequencies through different observation networks. Two sets of networks are considered in the analysis. The first set comprises undirected networks that vary in terms of efficiency while the second set comprises directed networks that vary in terms of homogeneity of node degree.

We find that collective innovation (described as an exploration of a technology landscape) improves average fitness over independent search because information about better innovations can diffuse faster through the network at an early stage. Moreover, we find that efficient networks outperform inefficient ones in the first stages of the search, but, given their slower rate of information diffusion, some of the inefficient networks require additional independent search which can guarantee a marginally better performance at later stages. We also find that degree homogeneous and undirected networks achieve better fitness on average than heterogeneous and directed ones.

Finally, we apply our model to a simplified scenario of innovation diffusion in a financial system by using an empirical network obtained from the Thomson Reuters DealScan database. For this illustrative case study, our results are presented and discussed from the perspective of system-level technological innovation. We conclude by discussing implications for technological innovation and possible extensions.


T. Khraisha, R. Mantegna, Network structure and optimal technological innovation, Journal of Complex Networks (2019) cnz020


Rosario Mantegna

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