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.
Just accepted for publication in the journal "Entropy". See https://www.mdpi.com/1099-4300/21/9/901 for the article.
We are proud to anounce that our paper introducing the Open Source python package git2net received a Special MSR Mention at the 16th International Conference on Mining Software Repositories (MSR) 2019 in Montreal, QC, Canada.
With git2net we introduce an Open Source tool that facilitates the scalable extraction of time-stamped co-editing relationships between developers in large git-based software repositories.
Click here for more information.
Wissenschaftler sollten sich öfter in öffentliche Debatten einmischen. Dazu muss die Diskussionskultur entschieden besser werden.
Dazu ist am 13. Februar 2019 in der Süddeutschen Zeitung ein Beitrag von Prof. Schweitzer als "Aussenansicht" erschienen.
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
Together with GESIS and Nokia Bell Labs we are organizing the Euro CSS.
The symposium will take place from 2 - 4 September 2019 at ETH.
For more information, please visit the official website here.
What regulates the careers of scientists? Where do they transfer and why? We have recently developed a data drive agent-based model that tackles these questions. By extracting from MEDLINE careers' paths of scientists, we are able to use these paths to reconstruct the global mobility network of scientists at city level. After calibrating our model against real-world data, our model correctly reproduces various topological properties of the mobility network only using two parameters! Read the full article here.
In this paper, we propose an efficient and practical method to identify valid bug reports which a) refer to an actual software bug, b) are not duplicates and c) contain enough information to be processed right away.
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.
We're hosting a half-day workshop on multi-edge network inference in R using the ghypernet-Package. more
Mobility of researchers is thought to aid the diffusion of scientific expertise. Restrictive migration policies, however, have the potential to disrupt the international exchange of knowledge and could negatively affect scientific progress. A fundamental understanding of the mobility patterns is necessary to support evidence based policy decisions.
Join us at WEHIA 2019 from 24-26 in London to discuss this highly relevant topic.
Click here for more information.
Traditional research has investigated the controllability of complex networks - a property whether a system can be steered from an arbitrary initial state to any desired final state with admissible external inputs in a finite time. To implement control in practice, the trajectory (route) to reach the final state must be systematically understood. Here we uncover the relations between the trajectory and several key factors, such as the control time, distance between the initial and final states, and the number of nodes receiving external control inputs.