In the research team of Systems Design (in German: Systemgestaltung, abbreviation: SG), we aim at the scientific description, modeling and computer simulation of “systems” from a theoretical perspective. This includes conceptual issues of systems thinking and systems engineering, formalization of systems dynamics, as well as quantitative approaches for nonlinear dynamical systems. In particular, we apply various methods to investigate COMPLEX SYSTEMS which emanated over the last 30 years from different fields such as statistical physics, evolutionary biology, micro economics, or computational sciences.
Based in the Department of Management, Technology, and Economics (D-MTEC) of ETH Zurich, our research in systems design is focused on socio-economic “systems”, such as business companies or social organizations, rather than on the design of computer systems or products. In more general terms, we are interested in a fundamental understanding of the DYNAMICS OF ORGANIZATIONS.
Some of the questions we address:
- What is the role of selforganization for the structure and the dynamics of organizations? Are there general evolutionary principles for organizations?
- How can the dynamics of organizations be explained from the interaction of their subunits (e.g. individuals, groups)?
- How do boundary conditions or network structures affect the formation of hierarchies in organizations?
- To what extent does the outcome of collective decision making depend on communication constraints such as limited access to information, incomplete or delayed information?
- What are the limitations to predict the dynamics of organizations, and how can that be used to estimate risk?
- How can robustness and adaptivity – two important features for the sustainability of organizations – be described quantitatively? How should organizations be designed to cope with that?
In order to find answers to the questions mentioned above, we use a wide range of methods, based on:
- paradigms and guiding principles of system theory
- statistical physics of many particle systems
- modeling approaches of distributed artificial intelligence (DAI)
- computer simulations of multi-agent systems
- analytical methods for nonlinear dynamical systems
- statistical methods for time-series analysis
- evolutionary optimization
In our genuinely interdisciplinary team, researchers with a very different, but quantitative background (physics, mathematics, computer sciences, engineering, economics, social sciences) jointly collaborate. We also have close collaborations with leading teams all over the world.