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. 


Die Entscheidungshelfer

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.
Den entsprechenden Beitrag finden Sie hier.


Article in notabene

In February 2019, the journal notabene published an article about the reformation. Our project to visualize the reformers' correspondence network is presented there.

You can find the article (in German) here.


Open PhD Position in Computational Social Science

We welcome applications for a doctoral position in Computational Social Science.
We offer excellent working conditions in a lively interdisciplinary team as well as a competitive salary and cooperations with leading institutions worldwide.

More information on this position and how to apply is available here.


Publication in Physics Today

To the extent that individuals interact with each other in prescribed ways, their collective social behavior can be modeled and analyzed.

To learn more about this, read Prof. Frank Schweitzer's article "Sociophysics" which has just been published in the journal Physics Today.


upcoming SG Seminar

Our next SG Seminar will take place on 27 February

Prof. Tomaso Aste (University College London, UK)
Network approach to machine learning

10 am - 12 pm
HG E 26.3, Rämistrasse 101, Zürich


Data Science in Industry

We're proud to see what former members of our chair achieved in industry. At the podium discussion about Data Science in Industry on Wednesday, 21st of November 2018, Dr. Antonios Garas, Dr. Pavlin Mavrodiev and Dr. Mario Tomasello, among others, talked about their everyday live while working in industry. They talked about the skills they brought to the job, the ones they acquired on the fly, and the ones that make them successful in reaching their goals.
The discussion was led by another former employee, Dr. Rebekka Burkholz.


h-core for ICSE paper

We are proud that our ICSE 2013 paper on triaging bugs has reached h-score in the Google Scholar Metrics as one of the most cited ICSE publications in the last 5 years. 

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.


Systemic risk on finite networks

How big is the risk that a few initial failures of nodes in a network amplify to large cascades? Predicting the final cascade size is critical to ensure the functioning of a system as a whole.

To make this prediction, we often compute the average cascade size using local tree approximations or mean field approximations. Yet, as we demonstrate in our recent work, in finite networks, this average does not even need to be a likely outcome. Instead, we find broad and even bimodal cascade size distributions.


The trajectory to achieve control

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.


New preprints on gHypEGs

In our latest articles, we have provided a formal presentation of the generalised hypergeometric ensemble of random graphs (gHypEG), and we have introduced a new family of block models based on it that are naturally degree-corrected: the block-constrained configuration model (BCCM).

You can find the two preprints here and here.


Reproducing size distribution of R&D alliances

We have recently developed a new model to reproduce the size distribution of R&D alliances among firms. Our model can be used not only for agent-based simulations, but it is also analytic tractable. In addition, we have tested it against a data set listing 15,000 firms engaging in 15,000 R&D alliances over 26 years. Interested? Then take a look at our paper.


From Big Data to Computational Social Science

The "discovery" of reguralitiries and correlations from big data cannot replace the scientific clarification of the hidden causal effects. Hence to make real use of big data, social scientists are indespensable to make computational science a social one.

Read more about this on the ETH Zukunftblog.