Research Activity at the Chair of Systems Design
Systems Design Data Driven Modeling
What kind of research do we do at the Chair of Systems Design? `Systems Design' has various meanings, for example, in computer science, where it refers to the architecture of computer systems. Engineering sciences also have their own version of systems design with particular emphasis on product development. None of these topics is really at the heart of our research agenda, although we cover some issues related to systems engineering in our courses.
Being part of the Department of Management, Technology, and Economics (D-MTEC) of ETH Zurich, the main focus of our research is on social and economic systems, for example, online communities with a large number of users, networks of firms and banks, or organizations in general. We are interested in a fundamental understanding of their structure and dynamics, to mitigate important societal problems, such as systemic risk or inefficient (suboptimal) solutions resulting from the dilemma between individual and collective utility maximization. Can we `design' the structure and dynamics of socio-economic systems such that a desired outcome is obtained, for example, cooperation is enhanced, or knowledge is shared in a more efficient way? This implies that we should know more about the structure and the dynamics within these systems and about the way they are built up by their constituents, grow and adapt to changing outside conditions.
Our methodological framework to address these `big issues' is given by the theory of complex systems, which has been developed over the last 40 years in different scientific disciplines, such as statistical physics, evolutionary biology, micro economics, and computational sciences, before it merged into a commonly accepted approach to investigate systems at large. Complex systems are comprised of a large number of strongly interacting subsystems -- entities, processes, or so-called `agents'. Their interaction results in the emergence of systemic features which cannot be inferred from the properties of the agents. Thus, the fundamental challenge of complex systems theory is the relation between the properties of the agents and their interaction on the `micro' level as well as the properties of the system as a whole on the `macro' level. A useful theoretical perspective to formalize these questions is provided by the `Complex Network' approach, where agents are represented by nodes, and their interactions are represented by links. Both agents and links may change over time, often on different time scales. Hence, the dynamics of the system can be seen as a coevolution of the agents and their network.
The complex network approach allows us to utilize a wealth of methods recently developed in different areas, to investigate for example biological or social networks. However, it is important to note that our research starts from identifying the socio-economic questions rather than proceeding from methods and tools. Empirical research plays a crucial role in our research agenda, in particular the analysis of large data sets about firm ownership, patent sharing, user ratings, or animal societies. Only the insights we obtain about the structure and the dynamics of these specific social or economic systems allow us to specify formal models of interacting agents to be simulated, or mathematically analyzed.