The workshop is co-organized by CSH Faculty member Frank Neffke and will take place on October 16–19, 2022 at the Complexity Science Hub.
The expansion of collective knowledge, i.e., of knowledge that is not just held by individuals but that can be aggregated across individuals, entities and spatial units, has been the driving force behind increased prosperity and is probably our best tool to fight the unintended consequences thereof. Scholars have debated the role of knowledge in the economy for decades, if not longer. Recently, machine learning and natural language processing has started helping to create large-scale datasets that describe not just the quantity, but also the contents of patents, scientific publications, workers’ skills, educational tracks, corporate product portfolios and so on, in ways that were unimaginable just five years ago. Nevertheless, these often high-dimensional and multi-scalar descriptions of knowledge held by individuals, teams, organizations or even countries often are hard to translate into the desired and more comprehensible low-dimensional concepts that our theoretical models rely on. As a consequence, empirical research either struggles by trying to squeeze the richness of the data into the narrow categories of current theories or gets stuck in descriptive analyses that do not generate a deeper understanding of the subject matter. Because collective knowledge is studied across fields, this problem is shared among multiple disciplines. In this workshop we therefore want to bring together scholars who study collective knowledge in the broad sense in management, economics, economic geography, innovation and related fields to take stock of the state of the art and sketch an agenda for future research.