3. Economic Complexity and Innovation

Coordinators: Robin Cowan and Önder Nomaler

Innovation, as the outcome of interlinked processes of learning and knowledge creation, is a particular manner in which economic agents ‘adapt’ to their ever-changing environment. Accordingly, this research theme, in close correspondence with the other innovation-related UNU-MERIT themes, treats innovation from the perspective of ‘complex adaptive systems.’

Complexity is a property of particular dynamic systems, the constituent components of which interact in non-linear ways. Behavior of complex systems cannot be understood by merely analysing their separate components (i.e., agents) which are strongly linked by feedback relationships: The agents’ interactive behavior influences the system as a whole while the system, in turn, influences the behavior of the agents (i.e., adaption). Complexity theory aims to explain how such self-organising systems can perform a variety of complicated tasks while displaying regular and (due to emergent phenomena such as path-dependence, tipping, and irreversibility, only to a limited degree) predictable behavior at the system level; Just like a living cell, an ecosystem, the weather and social/economic organisations.

The theoretical and empirical analyses of complex adaptive systems (In close proximity to evolutionary economics) typically count on computer modelling (e.g., agents-based simulations), network (graph) theory, and machine learning methods.

Drawing on the conceptual and methodological ideas of complexity theory, this research theme starts from the premise that knowledge itself is a complex object, and that any “piece” of knowledge, at the least in use, is connected to many other pieces of knowledge. This is true no matter the level of aggregation, whether we speak of individual artefacts or innovations, innovation systems, technological paradigms, or even the agents involved in creating and diffusing new knowledge. This research theme looks into innovation processes, causes and effects from micro to macro, and from basic research and the science system to applied research and firm interactions, to economic growth, structural change and development.


    Examples of ongoing research projects and topics:

    • Önder Nomaler and Bart Verspagen are using the PATSTAT database to map global technological trajectories using a big-data perspective. This project, which is funded by the European Patent Office, puts special emphasis on “green” technologies.
    • Neil Foster-McGregor, Önder Nomaler and Bart Verspagen are using the complexity perspective in a project funded by the Asian development Bank on the economic effects of integration in the Greater Mekong Subregion.

1. Economics of Knowledge and Innovation
2. Structural Change and Economic Development
3. Economic Complexity and Innovation
4. Governance and institutions
5. Innovation and Entrepreneurship for Sustainability Transitions
6. Migration and Development
7. Social Protection
8. Population, Development and Labour Economics