The Role of Network Topology and the Spatial Distribution and Structure of Knowledge in Regional Innovation Policy: A Calibrated Agent-Based Model Study

Recently, new regional innovation policy paradigms emerged that transcend a long-lived dispute on whether either regional specialization, diversification, or rather related variety is most conducive to regional innovativeness. This includes ‘smart specialization’ in which regions are deliberately specialized and connected following technological relatedness, ‘gatekeepers’ in which there are pipelines between dense regional networks, and a ‘regional mix’ of knowledge that allows sustained ‘branching’. We develop and use an agent-based model to study the conjecture that, to stimulate (supra)regional innovativeness, social planners need to consider both, in conjunction, the mix of technological knowledge in regions and the (inter)regional innovation network topology. We use this agent-based model to evaluate the performance of and study the internal mechanisms of these new policy paradigms in a variety of scenarios. To increase the external validity of our findings, we calibrate the knowledge graph searched by the agents in our model to the OECD patent database. We confirm that access to related variety is important, yet that, on top of that, access to incidentally related knowledge is crucial to prevent high-level lock-in and ensure long term technological progress. Moreover, we find that networks with regional gatekeepers are particularly innovative, because these gatekeepers form ‘knowledge hubs’, create short paths to potential partners, and enlarge the total pool of knowledge. In case agents have few relationships, we find exceptionally high performance for the gatekeeper network in combination with regional diversification. The smart specialization network is a solid second option, although it lacks access to incidentally related knowledge and thus will ultimately fall behind whenever agents have relatively few relationships. The study elaborates on specific scenarios to reveal intricacies in the relationship between knowledge distribution, network topology, and the structure of interrelationships between knowledge fields.