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. 


Resilience and performance of networked systems

Please find the program, abstracts and recorded talks at


Open PhD position in Computational Social Sciences and Politics

We invite applications for a PhD position in the field of Computational Social Sciences as part of our SNF-project on political speeches and networks. For more information and a chance to apply, click here.


International crop trade networks: The impact of shocks and cascades


Our most recent paper has been accepted for publication in Environmental Research Letters. To learn more, visit click the link.


What is the Entropy of a Social Organization?

Just accepted for publication in the journal "Entropy". See  https://www.mdpi.com/1099-4300/21/9/901 for the article.


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.


Who are the leaders and followers among bats?

Bats don't tell! But we find out.  See our two new manuscripts here: paper 1, paper 2



Improving the robustness of online social networks: A simulation approach of network interventions

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.


Modeling User Reputation in Online Social Networks: The Role of Costs, Benefits, and Reciprocity

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


Scientists' Mobility

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.


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.


Let's spend the night together?

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.


The law of proportionate growth and its siblings

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 emotions drive opinion polarization: An agent-based model

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.


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.