Welcome to the Chair of Systems Design
Our research can be best described as data driven modelling of complex systems with particular emphasis on social, socio-technical, and socio-economic systems. We are a truly interdisciplinary team of about 15 people from various disciplines (statistical physics, applied mathematics, computer science, social science, engineering). And, yes, we do all the cool stuff, from big data analysis to multilayer network models, from social software engineering to predictions of scientific success - not to forget our research on polarization in political systems, cooperation in animal societies, and life cycles of R&D networks. Just click through our publications, funded projects, teaching or media coverage.
Every researcher is affected by how scientific performance is measured. How should it be measured? Do we have the right data to do it?
If you eager to have answers to these questions, come to our Satellite Workshop Scientific Networks and Success of CCS 2020.
We are happy to announce that our Lead Agency Project "Signed Relations and Structural Balance in Complex Systems: From Data to Models" was granted by the SNF and NCN. We will carry out this project together with Prof. Holyst's group of Physics in Economy and Social Sciences at Warsaw University of Technology (Faculty of Physics).
Dazu ist am 12. März 2020 auf dem Blog des Verbands Digital Humanities im deutschsprachigen Raum ein Beitrag von Ramona Roller erschienen.
Please find the program, abstracts and recorded talks at
Scientists at the CSH expand an old theory of balance to explain the emergence of hyperpolarization
A new model of opinion formation shows how the extent to which people like or dislike each other affects their political views —and vice versa. The resulting division of societies can even become a matter of life and death, as the current crises show.
The latest version of our R library
The library allows studying multi-edge networks using the framework of the generalised hypergeometric ensemble of random graphs. Install the package by running
Online social networks (OSN) are prime examples of socio-technical systems in which individuals interact via a technical platform. We study the emergence of large drop-out cascades of users leaving the OSN by means of an agent-based model. Our aim is to prevent such drop-out cascades by influencing specific agents. We identify strategies to control agents such that drop-out cascades are significantly reduced, and the robustness of the OSN is increased. Read more on arXiv.
Intuitively, one would expect that higher costs lead to more users leaving an online social network (OSN) and hence decrease its robustness. We demonstrate that an optimal cost level exists, which maximizes both the performance of the OSN, measured by means of the long-term average benefit of its users, and the robustness of the OSN, measured by means of the life-time of the core of the OSN.Read more: http://arxiv.org/abs/1909.04591
HONS is the NetSci satellite for researchers that try to understand what we miss when we analyze graphs and network abstractions of complex systems. Its focus is on cutting-edge Higher-Order Network modelling techniques, which generalize network science techniques to models that account for higher-order dependencies in data on real systems.
The RStudio blog has listed our
Bechstein's bats form groups of different size to spend the day together in several roosts. At dusk, these groups dissolve, at dawn they may re-merge. So, what is a typical group size? And how long does a group use the same roost? We answer these questions by analyzing empirical data from two colonies. What is more, we also provide an agent-based model to reproduce these findings. See our paper here.
We combine the law of proportionate growth with additive growth terms, to develop an agent-based modeling framework with vast applications in social and economic systems. The paper discusses phenomena as diverse as saturated growth, competition, stochastic growth, investments in random environments, wealth redistribution, opinion dynamics and the wisdom of crowds, reputation dynamics, knowledge growth, and the combination with network dynamics. Read more on ArXiv
How to explain the emergence of collective opinions not based on feedback between different opinions, but based on emotional interactions between agents? The answer is given in our new publication on ArXiv.