Abstract
We analyse the autocatalytic structure of technological networks and evaluate its significance for the dynamics of innovation patenting. To this aim, we define a directed network of technological fields based on the International Patents Classification, in which a source node is connected to a receiver node via a link if patenting activity in the source field anticipates patents in the receiver field in the same region more frequently than we would expect at random. We show that the evolution of the technology network is compatible with the presence of a growing autocatalytic structure, i.e. a portion of the network in which technological fields mutually benefit from being connected to one another. We further show that technological fields in the core of the autocatalytic set display greater fitness, i.e. they tend to appear in a greater number of patents, thus suggesting the presence of positive spillovers as well as positive reinforcement. Finally, we observe that core shifts take place whereby different groups of technology fields alternate within the autocatalytic structure; this points to the importance of recombinant innovation taking place between close as well as distant fields of the hierarchical classification of technological fields.
Highlights
A large body of research on complex systems—physical, biological and socio-economical—has focused on the relation between the structure of interactions within heterogeneous populations of agents and the dynamic properties of the aggregate2018 The Authors
A relevant feature of these ecological systems is the presence of autocatalytic sets (ACSs) [4]—self-sustaining subsystems, in which each species benefits directly or indirectly from its cohabitation with the others
The relevance of interactions in the above framework lends itself to a complex systems interpretation for which networks are a natural tool of analysis
Summary
A large body of research on complex systems—physical, biological and socio-economical—has focused on the relation between the structure of interactions within heterogeneous populations of agents and the dynamic properties of the aggregate. Combining knowledge from previously isolated domains has become extremely relevant in several innovation-oriented domains, such as academic research projects—which often involve scientific collaborations between groups with heterogeneous backgrounds [14]—and industrial endeavours—where R&D collaborations have become common practice [15,16,17] especially in sectors characterized by a quick pace of technological progress (e.g. biotechnology [18] and information technology [19]) For this reason, the network structure of both scientific [20,21,22] and industrial collaborations [23] has been studied in depth in the past.
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