Model created by CSH scientist Samuel Martín-Gutiérrez and colleagues captures social division in multiparty democracies. The US could also benefit from it.  

Austria, India, Mexico, and Spain. As multiparty democracies, these countries present a more complex picture of what brings people together – or divides them. Using social media data, a group of researchers proposes a novel approach to measuring polarization in nations with multiple political parties.


Complexity Science Hub scientist Samuel Martín-Gutiérrez and his colleagues developed a model that infers opinions from social networks, and measures the left-right political division, as well as other more nuanced sources of tension. They analyzed Twitter data from the Spanish elections of 2015 and 2019.


“It’s important to understand polarization in our society, and our findings shed new light on how political beliefs are shaped,” says Martín-Gutiérrez. “Our approach can lead to useful insights when applied to real-world debates happening on social media.”


The findings of the study were recently published online in the journal Chaos, Solitons & Fractals.


The approach could be used in a variety of situations to identify the main points of disagreement. It could also be used in countries with two-party systems, such as the US. The primaries, a key part of the American voting system, are an excellent example, points out Martín-Gutiérrez. “There are several candidates in the presidential state primaries. Our model could be helpful in tracking and understanding how party members’ opinions are distributed” says the CSH researcher.

Neutral and unbiased

A key characteristic of the model, according to Martín-Gutiérrez, is its neutrality and unbiasedness. “We assumed we didn’t know anything about the [main Spanish] political parties in the study. And our findings demonstrate that a rigid classification of parties into pre-defined dimensions may not paint the most complete and accurate picture.”


Using Twitter data from two elections, Martín-Gutiérrez and his colleagues from the Universidad Politécnica de Madrid tested the proposed approach in practice. First, they explored the Spanish general elections of December 2015. In a second moment, they dissected the Spanish general elections of April 2019.


“In 2015, we had four opinion poles corresponding to the four main political parties: People’s Party (PP), Spanish Socialist Workers’ Party (PSOE), Podemos, and Citizens (Cs). In 2019, we have five opinion poles, with the addition of Vox,” explains Martín-Gutiérrez. 


As a first step, the team identified the opinion leaders, and their ideological positions, on Twitter. “The second step was to analyze the listeners, the interesting part of the network, and assign an opinion to a user according to the average opinion of the people they retweet”, says Martín-Gutiérrez.


Since the social spectrum is more complex and colorful than black and white, the researchers determined the same distance between every pair of poles to avoid bias. “In some contexts, it may seem more appropriate to place certain poles close together. For example, in an election certain political parties may be more ideologically aligned with each other than with the rest. However, parties that share ideology also compete for the same electoral base, often leading to even greater antagonism between them,” explain the authors of the study.

New knowledge

According to them, the opinion patterns of both analyses were consistent with the underlying Spanish social reality and could be interpreted taking into account their context. “There is a clear picture of the Spanish political spectrum, and we can see, for instance, where the opinion distribution is most stretched or where the average opinion is located,” says Martín-Gutiérrez.


“Our findings seem to be in line with what we intuitively know about Spanish politics. From this we can start building new knowledge, for instance, about citizens’ perceptions of politics and their behavior.”


In the visualization created by Liuhuaying Yang, CSH visualization expert, you can explore the opinion distribution of the 2015 Spanish elections: 

(c) CSH
Opinion distribution of the 2015 Spanish elections: a cloud of users’ opinions with its center of mass marked with a red square and the principal components as arrows with lengths proportional to their explained variances

The proposed method could also assist policymakers in dealing with polarization and healing social rifts. “It could help policymakers devise de-escalation interventions by addressing the issues that cause the strongest tensions,” argue the authors of the study. 


Furthermore, it could be used to combat online misinformation spread by bots and trolls. The model could be used to evaluate the influence automated bots have on social media as compared to real human accounts.


The study “Multipolar social systems: Measuring polarization beyond dichotomous contexts” is available online at Chaos, Solitons & Fractals

A recent study makes it clear: Countries like Sweden with the ability of linking data from different areas – such as the labor market and health care – have a decisive advantage when it comes to setting targeted actions.

A research team from the Complexity Science Hub, together with scientists from Sweden, Denmark and the Netherlands, investigated the extent to which mental and somatic illnesses influence integration into the labor market and whether there is a difference here between refugee and Swedish-born young adults. “In total, we analyzed data from 41,516 refugees and 207,729 Swedish-born people aged 20 to 25 from 2012 to 2016,” explains Jiaying Chen from the Complexity Science Hub and the Medical University of Vienna.


In both groups, multimorbidity (the simultaneous presence of several disorders) has a negative impact on labor market opportunities. Both mental and somatic disorders pose a risk that labor market integration will not succeed. However, this effect is more pronounced among young refugees. They have a higher risk of being and remaining unemployed than those born in Sweden. However, the strongest risk factor for long-term unemployment is refugee status per se, the study finds.


In addition, refugees have a lower chance of receiving disability pensions. While 7.2 percent of young Swedes with mental disorders receive disability pension, only 5.5 percent of those who have had to flee benefit from it. 


“This is a very robust result that has already been supported in many studies,” explains Peter Klimek of the Complexity Science Hub. “It suggests that refugees, who have often experienced massive trauma, are at risk of poorer access to the health care system. This implies that they often need to have a higher level of severity before a disease is diagnosed,” Klimek says. 


Poorer labor market integration can therefore also be exacerbated by the fact that poorer health already exists at the time of diagnosis, he adds. “With targeted public health actions to specifically address this group and to sensitize treating physicians, Sweden could bring about an improvement in the situation,” explains Ellenor Mittendorfer-Rutz from the Swedish Karolinska Institutet, who coordinated the project.


In other countries like Austria similar findings could not be obtained at all. Due to different social and health care systems, as well as different origin areas of refugees, the results of this study describe the situation in Sweden. However, they can only be transferred to other countries to a limited extent.


A good data infrastructure for research purposes is crucial. In Sweden, Finland and Denmark, there are already developed registry landscapes that allow, for example, de-identified information on health, labor market integration and refugee status to be centrally linked and securely evaluated. This ability of linking data allows researchers to analyze the interplay of these three factors.


The findings support decision makers to take action to improve the situation where appropriate – such as implementing strategies to promote the inclusion and participation of young refugees in the labor market, reducing social disadvantage within the refugee population, and potentially improving the economic stability of these countries.


“In Austria, a similar study could not be conducted. On the one hand, there is a lack of complete data. For example, there is no diagnosis recording in the private practice sector. And on the other hand, data are pseudonymized differently in various areas, which is why linking the data is not possible here.,” Klimek explains.

Sándor Juhász is the first Marie Sklodowska Curie postdoctoral fellow at the Complexity Science Hub (CSH). In the next 20 months, Juhász will be delving into tax data. His goal is to find new ways of supporting local economic development.

You define yourself as “an economist interested in data magic, networks, innovation and (economic) geography.” Is it possible to perform magic with data?

Of course. Raw data files are messy and sometimes even unstructured. If you can turn it into very attractive and easy-to-understand figures at the end, the message is delivered quickly. To be able to do this with a single figure? I think this is magic.

Can you tell me what magic tricks you are going to perform at CSH?

Until now, value-added tax (VAT) information [a general tax that, in principle, is applied to all goods and services] has been used to study, for instance, economic shock propagation and supply chain resilience at the national level. However, few people have used this data for testing classic economic geographic topics or supporting local economic development policies.


I will focus on the geographical aspect of VAT networks during my stay. It is my aim to use tax data to develop tools or services where people can understand how a particular industry in a particular region depends on other industries and regions in the country, for example. It could be used to monitor the local economic environment and connections. By understanding the economic connections better, I hope we can turn this data into a tool that can support local economic development strategies. 

What made you choose CSH as your host institution for the fellowship?

The main reason is that my entire project revolves around Hungarian VAT data. And I believe CSH has a good know-how about the supply chain economy and how VAT data can be transferred into meaningful projects. 


My supervisor, Frank Neffke, was another factor that drew me to CSH. We work on similar projects and he is the kind of mentor a Marie Curie fellow needs: an experienced senior researcher. Having the opportunity to work in Vienna, which is close to my hometown of Budapest, was also a plus.

While at CSH, what are your plans?

I plan to write two research papers during the project. In addition, I plan to create an intuitive visualization site where people can check, for instance, the connections of an industry in a specific city in Hungary. This will enable them to find out who are its main suppliers and buyers. For that, I believe that CSH is the right place to be. I am looking forward to collaborating with Liuhuaying Yang and others and learning about data visualization practices. My goal is to learn as much as I can during my stay.


This visualization site could be especially useful for policymakers, as it intends to provide an overview of the local structure of the economy. It will show how flows occur across the country, and across industries. It will give a new geographic perspective on the economy, in my opinion.

Any other magic tricks? 

I am interested in two main areas of research. One is socio-economic networks, i.e. supply chains and the economic interactions between agents. The other is urban data science, namely how people move around a city and how locations are organized in cities. 


My future plan is to combine both areas and conduct very fine grain, city level studies that incorporate social interactions as well. I find that challenging, but also fun!

Would you mind giving me an example?

I am interested in studying how industries collocate in regions. We believe that they collocate in a specific region because certain channels, like input-output resources and skilled labor, or accumulated knowledge, are present there. Therefore we assume industries collocate there because they get to use the pool of resources and this reinforces spatial concentration. In the future, I would like to examine whether similar channels and reasons are important for certain shops, amenities, services, or firms to collocate inside urban neighborhoods. I am very interested in understanding how things are located at the micro level inside cities. 

What do buttons, dartboards, a Black Forest cake and the Colosseum have in common?


Exactly, they are round. More precisely, circular. Or even better: If you measure their circumference and divide it by their diameter, you always get one number: π, pi.


Pi (π) is one of those famous constant numbers that keep popping up in all sorts of different fields. Many calculations, many formulas, and most things that feature a circle ultimately depend on its 3.141592…. Even those who are searching for Nessie bump into .

Finding Nessie

Imagine a person trying to take the best photo of Nessie, the Loch Ness monster. Hundreds of people have tried it. The lake itself is quite large (a surface of 56 km2). So, not an easy task. Where would you start looking for Nessie? The best technique: where it was last seen. At least you know that Nessie once stayed  in that part of the lake. 


World Pi Day at Complexity Science Hub


The issue is that the monster moves, and even if it moves slowly, the area that we have to search grows pretty quickly. For example, if Nessie moves at a speed of 1 km per hour, what is the area we need to search? Just one hour after the last known report, the monster could be anywhere within a circle with an area of 3.14 km2. And in two hours, the area will be 12.6 km2. So the area where the monster is sure to be after h hours since its last sighting is πh2.

Pi (π) in cities and people

Let’s leave Nessie alone again, nevertheless appears almost everywhere. Besides circles and countless mathematical formulas, you can find it playing pool. Even in the Bible. As well as in cities. And it works like this:


If you randomly distribute points in a circle with radius r, you you may be (more or less) stunned (depending on whether you are stunned by at all) to find the following: The average distance between points is 128r/45. The same formula is used over and over to model various aspects of urban transportation and traffic flow. For more than 40 years, we have been modeling cities using the same formula.

Commuting by pi

If you know the area of a city, say A, then traveling from one location to another will grow according to 128√A/45π. This formula holds for cities that are perfectly circular (perhaps a very simple city!) and where you can travel in a straight line between any two points. Of course, no city is like this, but it gives us an idea of how long trips will be and how long the commute will take.


Vienna, for example, has an area of roughly 415 km2. So if we take any two locations in the city, they are on average 18 km apart. However, Innsbruck has a surface of 105km2, so on average the places are 9 km apart. This is shown in a recent study by Rafael Prieto-Curiel of the Complexity Science Hub, who, together with colleagues, measured about 183 million buildings in African cities. He used a similar formula to the one for the distance between points inside a circle can also be used to measure distances between buildings in a city.

World Pi Day at Complexity Science Hub

There are other aspects of cities, including their irregular shapes, the space between its buildings, the many roads or its fractal patterns. However, keeps appearing! 


Last but not least, does not even stop at the city boundaries. Instead, the fascination with lives in the heads of people. This culminates in worldwide competitions  in which people try to outdo each other in enumerating more digits of . The official world record is currently 70,030 digits. It took Suresh Kumar Sharma 17 hours and 14 minutes.


This was in 2015, and no one has officially beaten it since. But developer Emma Haruka Iwao announced another record on June 8, 2022: a program from Google calculated the circular number to 100 trillion digits. That took 157 days. By way of comparison, in 1940 scientists were only able to calculate around 1,000 decimal places.


Happy day! A day for mathematicians, people who like math, or even people who suffered from in school but can still remember the first digits of 3.14!

In what ways will the collapse of Silicon Valley Bank (SVB) – and Signature Bank and Silvergate  – affect cryptocurrencies?


Crypto expert Bernhard Haslhofer, from the Complexity Science Hub, provides a brief analysis of the destabilization of cryptoassets

The collapse of Silicon Valley Bank (SVB) and the destabilization of stablecoins


It is yet unclear to what extent the collapse of Silicon Valley Bank (SVB) will cause contagion effects and affect other banks. However, it is already clear that recent events coincide with a remarkable event: the destabilization of stablecoins.


Stablecoins are specific cryptoassets backed by real-world currencies like the USD or the EUR. Prominent examples are USDC and Tether. Both have a market capitalization of more than 100 billion USD. The promise is that a customer owning one unit of a stablecoin can exchange it for one fiat currency unit (e.g., USD). The companies controlling the underlying asset management mechanism typically deposit their fiat holdings in banks. According to recent (yet unconfirmed) news, Circle, the company behind USDC, deposited roughly 3.2 billion USD at SVB [1]. That means part of the stablecoins holdings might have become de-pegged.


Coinciding with recent events around SVB, we are noticing that almost all major stablecoins have lost their peg to their underlying fiat currencies. USDC, for instance, reached a low of 0.8, which essentially means that one could buy 1 USD for 0.8 USD. This, of course, is an indicator for a major loss of trust, which also propagates across other stablecoins [2].


In our recent paper, we have shown that stablecoins are a cornerstone of many different DeFi protocols [3], which could be affected by run-on or a collapse of major stablecoins.

Word lists are the basis of so much research in so many fields. Researchers at the Complexity Science Hub have now developed an algorithm that can be applied to different languages and can expand word lists significantly better than others.

Many projects start with the creation of word lists. Not only in companies when mind maps are created, but also in all areas of research. Imagine you want to find out on which days people are in a particularly good mood by analyzing Twitter postings. Just looking for the word “happy” wouldn’t be enough. 


Instead, you would have to use an algorithm that detects all tweets that indicate that someone is happy. “So the first step is to create a list of all the words that indicate just that. The whole research stands or falls on doing so,” explains Anna Di Natale, a researcher at the Complexity Science Hub in Vienna. But how to come up with the most accurate, complete word lists possible? 

A problem that concerns many

This widespread problem not only concerns opinion researchers who want to find out how politicians’ statements are received by the public. Companies, too, want to find out how their products are perceived through sentiment analyses.


To improve things, Di Natale has now developed a new method, called LEXpander, that outperforms previous algorithms. And this even in two different languages – German and English. Moreover, for the very first time ever, she has developed a way through which it is possible to compare different tools at all.

Improved performance

In comparison with four other algorithms for wordlist expansion (WordNet, Empath 2.0, FastText and GloVe), LEXpander performed significantly better, especially in German. For example, the researchers found that LEXpander guesses 43% of words right when expanding an English word list for positive meaning. A very popular model, FastText, in comparison, is right only 28% of the time.

Complexity Science Hub researcher Anna Di Natale finds a new and better way to create word lists

Independence from the language itself

The reason is that this tool works language-independently. It is not based on one language, but on a so-called colexification network. This recognized linguistic concept resides on homonyms and polysemies, single words that have two or more distinct meanings. For example: the ancient Greek word φάρμακον (pharmacon) can mean medicine or poison. Two different things, but thematically close. But there are others that don’t suggest kinship – such as “bank” as a financial institution or the land alongside a river. 


“If you collect them across many languages – and here we analyzed about 19 different languages – you can see connections between them,” Di Natale says. The network is formed when these colexifications occur in several languages across different language families, creating connections.


This independence from the language itself allows LEXpander to achieve better results in different languages. “There are many methods developed for English. They work very well and quickly and everyone uses them. Trying to apply them to other languages works, but not as well as it might work if you had started developing a method for German or Italian,” Di Natale explains. 

Especially important for new topics

For many topics there are already good word lists. But for new topicslike when COVID came up – new ones have to be created. Until now, they were usually created by hand during brainstorming with colleagues and several tools were used to help. But until now there was no way to compare them. Anna Di Natale and her team have now created this possibility and have also developed a new tool that performs better than the others. This can be an important cornerstone for many future research projects in various fields.


The study “LEXpander: Applying colexification networks to automated lexicon expansion” has been published in Behavior Research Methods.

Spätestens seit der Pandemie, traten die globalen Wissenslücken über Liefernetzwerke deutlich zutage. Durch das neue Forschungsinstitut ASCII gegründet vom Complexity Science Hub, der FH OÖ, dem VNL und dem WIFO, sollen diese nun geschlossen werden. Österreich wird dadurch eine internationale Vorreiterrolle einnehmen. Peter Klimek, ASCII-Direktor und Forscher am Complexity Science Hub, dazu im Gespräch – über die Entstehung des ASCII, globale Zusammenhänge und seine persönliche Vision.

Peter Klimek at the Complexity Science Hub © EugenieSophie

Aus welchem Grund wurde das ASCII gegründet? Und warum gerade jetzt?

Peter Klimek: Erstens, um zu frühzeitig erkennen zu können, dass es zu Engpässen in einer Lieferkette kommt und somit noch bevor eine tatsächliche Versorgungsknappheit eintritt. Zweitens wollen wir diese Wertschöpfungsketten nicht nur resilienter, robuster und sicherer machen, sondern auch eine evidenzbasierte Grundlage für Entscheidungträger:innen bereitstellen und proaktiv aufzeigen, wie wir sie optimal umbauen können, sodass in Zukunft ein nachhaltigeres und effizientes Wirtschaftssystem entsteht.

Könntest du das näher ausführen?

Zu erstens: Wir wollen dazu beitragen künftig die Versorgungssicherheit zu gewährleisten. Das ASCII ist eine notwendige Konsequenz aus den Erfahrungen der letzten Jahre, wo wir multiple Krisen erlebt haben und deren Auswirkungen auf die Lieferketten und Produktionsnetzwerke deutlich spürten.


Wir konnten sehen, dass Dinge, die wir davor als gegeben gesehen haben, plötzlich nicht mehr funktionierten – zum Beispiel, dass es zu Lebensmittelknappheiten kam, dass Autoteile nicht lieferbar waren und vieles mehr. Wer gerade nach Großbritannien schaut, kann diese Konsequenzen in den Supermärkten unmittelbar sehen. Bislang hat man erst bemerkt, dass es Probleme in den Versorgungsnetzwerken gibt, wenn die Sachen „plötzlich“ nicht mehr verfügbar waren. Es gab keine Möglichkeit, eine Knappheit vorherzusehen.


Wir konnten zudem beobachten, dass Lieferketten sehr anfällig sind und enorm weitreichende kaskadierende Effekte haben. Einzelne Ereignisse wie eine Schiffshavarie können beispielsweise zu globalen Störungen in Wirtschaftskreisläufen führen. Wenn in diesen sogenannten „Flaschenhälsen“ der Liefernetzwerke Störungen auftreten, betrifft das in weiterer Folge die Versorgung von sehr vielen Ländern.

“Technologiewechsel braucht Anpassung”

Zu zweitens: Viele Zukunftsthemen hängen direkt mit Liefernetzwerken zusammen. Zum Beispiel: Wie schaffen wir es Emissionen zu reduzieren? Wie entsteht ein nachhaltigeres Wirtschaftssystem? Wie können wir eine klimaneutrale Mobilität garantieren? Wenn wir zum Beispiel vermehrt auf E-Mobilität setzen möchten, müssen wir die Industriestrukturen entsprechend umstellen.


Ein Technologiewechsel bedingt immer, dass sich auch die Produktionsnetzwerke daran anpassen müssen. In vielen Bereichen wissen wir derzeit jedoch noch nicht, wo wir hier an Limitationen stoßen – etwa wie sehr man neue Technologien nach oben skalieren kann, um eine ähnliche Effizienz zu erzielen.

“Viele ungelöste Fragen”

Das heißt, würden wir beispielsweise apodiktisch von oben entscheiden, dass es nur noch E-Autos geben soll, würde sich das im Hinblick auf die notwendige Infrastruktur überhaupt ausgehen? Dies sind bislang größtenteils ungelöste Fragen, da wir gar nicht wissen, wie diese Produktionsnetzwerke aktuell im Detail aussehen.


Wir könnten beispielsweise Maßnahmen ergreifen, um in Österreich weniger Emissionen in der Lebensmittelproduktion zu erzeugen. Wenn wir zum Ausgleich dann aber Lebensmittel importieren und nicht wissen wie diese produziert worden sind, wissen wir auch nicht, was sich in der globalen Bilanz ändert. Deshalb müssen wir zuerst ermitteln, wie diese globalen Kreisläufe funktionieren.

Wie sah die Lieferkettenforschung bislang aus?

Peter Klimek: Ein Großteil der Forschung über Logistik und Lieferketten geschah bislang aus Unternehmensperspektive. Einzelne Unternehmen, die ihre Kundenzahl, Zulieferer, Infrastruktur und Lieferketten optimieren wollen.


Auf Systemebene, also wie ein optimales Netzwerk auf globaler Ebene aussieht, dazu gibt es bislang wenige Forschungserkenntnisse. Die Gründe dafür: Einerseits gibt es die Daten dazu gar nicht. Und zweitens wurde bisher häufig aus der Perspektive eines einzelnen Unternehmens geforscht, ohne auf die Makroperspektive einzugehen. Das wollen wir nun am ASCII ändern.

Und was macht das ASCII anders?

Peter Klimek: Die Forschung auf Makroebene braucht einen Mix an Disziplinen, die jetzt am ASCII (Anm.: durch die Gründungsmitglieder Complexity Science Hub, Logistikum of FH OÖ, Verein Netzwerk Logistik GmbH (VNL) und WIFO) zusammenarbeiten. Denn wir müssen die Netzwerke und Logistik kennen, wir müssen mit Systemen umgehen können, die sehr komplex und verflochten sind und wir müssen all das in einen wirtschaftspolitischen Kontext setzen können – schließlich erfüllen diese Netzwerke eine ökonomische Funktion.


Die Expertise des Complexity Science Hub auf Datenebene wird dabei eine zentrale Rolle spielen, um aus dieser enormen Menge von Daten evidenzbasierte Erkenntnisse ableiten zu können. Darüber hinaus bringen wir am Complexity Science Hub bereits einen großen Erfahrungsschatz im interdisziplinären Arbeiten mit und sind es gewohnt datengetriebene Ergebnisse in verschiedenen Kontexten so aufbereiten, dass sie als Grundlage für fundierte Entscheidungen dienen können.

Wieso kam es scheinbar plötzlich zu diesen Lieferkettenausfällen in der letzten Zeit?

Peter Klimek:  Viele Expert:innen in den einzelnen Bereichen kannten auch davor die Schwachstellen. Doch einerseits wurden sie durch diese multiplen Krisen erst jetzt ins öffentliche Scheinwerferlicht gerückt. Denn durch die Globalisierung fand in vielen Fällen hauptsächlich eine Konzentrierung der Produktionsprozesse statt, was Liefernetzwerke natürlich anfälliger macht. Wie anfällig, aber tatsächlich, das ist jetzt deutlich geworden.


So wusste man beispielsweise schon seit Jahren, dass es zu einer immer stärkeren Konzentration der Medikamentenproduktion in bestimmte Länder kommt. Schlagend werden diese Dinge allerdings häufig erst, wenn tatsächlich etwas passiert, wie es in der Pandemie der Fall war.


Andererseits gibt es Expert:innen in bestimmten Feldern, aber ein systematisches Monitoring über eine Vielzahl von Wirtschaftsbereichen und eine gezielte Aufbereitung dieser Informationen fehlt.

Was werden die großen Herausforderungen für ASCII sein?

Peter Klimek: Wir müssen die Produktions- und Wertschöpfungsnetzwerke besser kennenlernen. Da es dazu keine vollständigen Daten gibt, werden wir sie aus vielen unterschiedlichen Quellen mühsam zusammensammeln müssen. Außerdem müssen wir, um die Abhängigkeiten von Österreich zu kennen, diese Netzwerke von Anfang an auf europäischer und in Wahrheit globaler Ebene denken.


In nächster Instanz wollen wir verstehen, wie sie sich verändern, wie anfällig sie für Schocks sind und wie wir sie so gestalten können, dass sie sicherer und nachhaltiger werden, ohne dabei Effizienz einbüßen zu müssen.

Wie kam es zur Gründung des ASCII?

Peter Klimek: Während der Pandemie rückte das Thema Versorgungssicherheit immer stärker in den Fokus, sodass entsprechende Krisenstäbe des Ministeriums eingerichtet wurden, in denen unter anderem auch der Complexity Science Hub vertreten war.


Schnell haben wir dort gemeinsam festgestellt, dass die „Intelligence“ – im Sinne einer übergreifenden Aufklärungskompetenz – in Österreich fehlt, um frühzeitig drohende Engpässe erkennen zu können. Auf Basis dieser Erfahrungen entstanden einzelne Projekte und schließlich die Idee eines Institutes, das alle notwendigen Kompetenzen in sich vereint.

Wie sieht die Vision für das ASCII aus?

Peter Klimek: Meine Vision ist, dass wir ein weltweit führendes Forschungsinstitut werden, das sich mit lieferkettenbezogenen Fragestellungen datenbezogen auseinandersetzt und dadurch Nutzen für die Bevölkerung entsteht.


Neben der evidenzbasierten Politikberatung wollen wir aber auch im Bereich der Wirtschaft ein Ansprechpartner für Unternehmen sein. Im Bereich der Forschung möchten wir die fehlende Datenlandschaft ergänzen und für die internationale Forschungsgemeinschaft aufbauen, weil natürlich viele Kolleg:innen vor dem gleichen Problem stehen. Durch die Gründung des ASCII haben wir die Gelegenheit eine internationale Vorreiterrolle einzunehmen. Diese möchten wir in den nächsten fünf Jahren durch wissenschaftlich exzellente Arbeit ausfüllen.

Global knowledge loopholes about supply networks have emerged since the pandemic. The Supply Chain Intelligence Institute Austria (ASCII) should now be able to close these gaps. In an interview, ASCII director and complexity researcher Peter Klimek discusses the origins of the new institute, global connections, and his personal vision.

Peter Klimek at the Complexity Science Hub © EugenieSophie

What was the reason for the foundation of ASCII? And why now in particular?

Peter Klimek: First of all, to be able to recognize early on that there will be bottlenecks in a supply chain and thus before an actual supply shortage occurs. Secondly, we want to make these value chains not only more resilient, robust and secure, but also provide an evidence-based foundation for decision-makers and show proactively how we can optimally transform them to create a more sustainable and efficient economic system in the future.

Would it be possible for you to elaborate on that further?

Peter Klimek: Regarding the first point, we want to help ensure future supply security. ASCII is a necessary consequence of the lessons learned in recent years, where we have experienced multiple crises and clearly felt their impact on supply chains and production networks. Suddenly, things we took for granted before stopped working — food shortages, car parts not being available, etc.


Right now, you can see these consequences directly in the supermarkets in the UK. Prior to this, people hadn’t realized there were problems in supply networks until things “suddenly” became unavailable. There was no way to predict a shortage.


We also observed that supply chains are very fragile and have enormously far-reaching cascading effects. Single events such as a shipwreck, for example, can lead to global disruptions in economic cycles. If disruptions occur in these so-called “bottlenecks” of the supply networks, this subsequently affects the supply of many countries.

“Many largely unresolved questions”

Regarding the second point: many future issues will be directly linked to supply networks. How can we reduce emissions, for example? How do we create a more sustainable economic system? What can we do to ensure carbon-neutral mobility? We have to change industrial structures if we want to rely more on e-mobility, for instance.


As technology changes, production networks must also adapt. We do not yet know where we will run into limitations in many sectors. An example would be how many new technologies can be scaled up to achieve similar efficiency levels.


In other words, would a top-down decision that only e-cars be allowed even be possible in terms of the necessary infrastructure? These are largely unresolved questions so far because we don’t even know what these production networks currently look like in detail. Austria could take measures to produce fewer emissions in food production, for example.


However, if we import food to compensate and do not know how the food was produced, we also would not understand how the global balance would change. Therefore, we must first find out how these global cycles work.

What has supply chain research been so far?

Peter Klimek: Most logistics and supply chain research has been conducted from a corporate perspective. Companies strive to optimize their customer base, suppliers, infrastructure, and supply chains. There has been only limited research at the system level, i.e., what an optimal network looks like globally. The reasons for this are, firstly, that the data doesn’t exist. Secondly, research has often been conducted from the perspective of a single company, without taking a macro perspective. At ASCII, we now want to change that.

What does ASCII do differently?

Peter Klimek: A mix of disciplines is needed for macro level research, and ASCII is bringing those disciplines together (note: through the founding members Complexity Science Hub, Logistikum of FH OÖ, the Verein Netzwerk Logistik GmbH (VNL) and WIFO). Because we need to know the networks and logistics, we need to be able to deal with systems that are very complex and intertwined, and we need to be able to put all this into an economic policy context – after all, these networks serve an economic function.


The Complexity Science Hub’s expertise at the data level will play a central role in deriving evidence-based findings from this enormous amount of data. In addition, we at the Complexity Science Hub already bring a wealth of interdisciplinary experience and are used to analyzing data-driven results in a variety of contexts so that they can serve as the foundation for well-grounded decisions.

Why did supply chain failures occur so suddenly?

Peter Klimek: Many experts in the specific areas were fully aware of the weaknesses beforehand. On the other hand, these multiple crises have only now brought them into the spotlight. Often, globalization has mainly led to a concentration of production processes, making supply networks more vulnerable. The extent of their vulnerability, however, has now become apparent.


For example, it has been known for years that there is an increasing concentration of the production of prescription drugs in certain countries. As in the pandemic, these things are often only noticeable when they occur.


While there are experts in certain fields, systematic monitoring across a wide range of economic sectors and targeted processing of information are lacking.

What will be ASCII’s major challenges?

Peter Klimek: We need to better understand the production and value networks. The data on this will need to be painstakingly gathered from many different sources since there is no complete set. Additionally, in order to understand Austria’s dependencies, we must think of these networks on a European and, in fact, global level.

How did ASCII come about?

Peter Klimek: During the pandemic, the issue of supply security came increasingly into focus, so corresponding crisis teams were set up at the ministry, in which the Complexity Science Hub was also represented, among others. There, we quickly realized together that Austria lacked the “intelligence”— in the sense of an overarching intelligence competence — to be able to recognize impending bottlenecks at an early stage. Based on these experiences, individual projects were developed, and finally the idea of an institute that combines all the necessary competencies.

What is the vision for ASCII?

Peter Klimek: My vision is that we will become a world-class research institute that addresses supply chain-related issues in a data-driven manner and thereby creates benefits for the population.


As well as providing evidence-based policy guidance, however, we also want to be a point of contact for companies in the business sector. Specifically, we want to build up the missing data landscape for the international research community, since many colleagues are facing the same challenge. We have the opportunity to play a pioneering role on the international stage by founding ASCII. In the next five years, we hope to fill this role through scientifically excellent work.

Lieferketten absichern, strategische Abhängigkeiten verstehen und nachhaltige sowie effiziente Produktionsnetzwerke proaktiv gestalten – mit diesen Zielen nimmt das Supply Chain Intelligence Institute Austria (ASCII) eine weltweite Vorreiterrolle ein.


Der Complexity Science Hub ist eine der Gründungsorganisationen, gemeinsam mit der FH OÖ, dem VNL und dem WIFO. Heute wurde das neue Institut im Bundesministerium für Arbeit und Wirtschaft (BMAW) vorgestellt. 

Supply Chain Intelligence Institute Austria (ASCII)

Globale, europäische und österreichische Produktions- und Logistiknetzwerke stehen vor großen und vielfältigen Herausforderungen. Die Krisen der letzten Jahre offenbarten Schwachstellen in Lieferketten und Produktionsnetzwerken. Es kam etwa zu Engpässen bei der Verfügbarkeit von Rohstoffen, Verpackungsmaterialien, Chips oder Kabelbäumen für den Autobau, was Verzögerungen in der Produktion und Lieferung verursachte. Das wiederum führte zu markanten, ökonomischen Schäden.


Diese Störungen, auch ansonsten resilienter Lieferketten, zeigen deutlich wie wichtig es sein wird, die Vielzahl von Fragen zur Gestaltung von nachhaltigeren, resilienteren Versorgungsnetzen zu beantworten“, erklärt ASCII-Direktor Peter Klimek, der am Complexity Science Hub wissenschaftlich tätig ist.


Um diese Fragen langfristig beantworten zu können, setzt das neugegründete Supply Chain Intelligence Institute Austria (ASCII) auf Interdisziplinarität“, so Klimek.

Gemeinsam mit ihm werden Wissenschafter:innen aus verschiedenen Forschungsbereichen und von unterschiedlichen Institutionen an datengetriebenen analytischen Werkzeugen arbeiten, um Lieferketten abzusichern und   die Versorgungssicherheit zu gewährleisten, wobei sie hier sowohl wirtschaftspolitische als auch unternehmerische Perspektiven miteinbinden werden.


„Ziel ist es, Entscheidungsträger:innen solide Informationen zu liefern, für die Erreichung der österreichischen und europäischen Ziele einer sicheren Wertschöpfung und Klimaneutralität“, so Klimek. Dieses Vorhaben wird in enger Zusammenarbeit zwischen den Gründungsorganisationen (Complexity Science HubLogistikum der FH OÖVerein Netzwerk Logistik (VNL) und WIFO) umgesetzt.

Dabei wird die Lieferkettenthematik mit übergreifenden Risiken assoziiert – wie zum Beispiel geopolitische Unstimmigkeiten oder Handelskriege, sowie mit den Auswirkungen des Klimawandels, dessen Bewältigung neue Produktionsstrukturen und Geschäftsmodelle erfordern wird.

Die Ziele des ASCII im Überblick:

  • Entwicklung eines umfassenden Datensystems zu Lieferketten und Produktionsnetzwerken
  • Monitoring von strategischen Gütern, sodass bereits frühzeitig interveniert werden kann, um Lieferengpässe zu vermeiden
  • Unmittelbare und praxisorientierte Wissenserzeugung im Bereich von Wertschöpfungsnetzwerken – deren Veränderungsprozesse und ihre Integration in den volkswirtschaftlichen Kontext
  • Internationale Ausrichtung und breite Kommunikation für zeitkritische und komplexe Fragestellungen im Zusammenhang mit Wertschöpfungsketten
  • Datenbasierte Handlungsempfehlungen für Entscheidungsträger:innen ableiten

Das Projekt wird vom österreichischen Bundesministeriums für Arbeit und Wirtschaft (BMAW) mit 7,5 Millionen Euro, sowie vom Land Oberösterreich mit 2,5 Millionen Euro unterstützt.

Securing supply chains, knowing strategic dependencies and proactively shaping sustainable, as well as efficient production networks — with these goals, the Supply Chain Intelligence Institute Austria (ASCII) aims to put Austria in a leading role worldwide. The Complexity Science Hub is one of the founding organizations of the institute, together with FH ÖÖ, VNL and WIFO. ASCII was launched at the Federal Ministry of Labor and Economics today.

Supply Chain Intelligence Institute Austria (ASCII)

The global, European and Austrian production and logistics networks are facing major and diverse challenges. The crises of the last few years revealed vulnerabilities in supply chains and production networks. For example, there were bottlenecks in the availability of raw materials, packaging materials, chips or cable harnesses for car manufacturing, which caused delays in production and delivery. This in turn led to striking, economic damage.


Such disruptions, even to otherwise resilient supply chains, clearly show how important it will be to answer the multitude of questions around creating more sustainable and resilient supply networks,” explains ASCII Director Peter Klimek, who is a research scientist at the Complexity Science Hub.

In order to find long-term answers to these questions, the newly established Supply Chain Intelligence Institute Austria (ASCII) is focusing on interdisciplinarity,” says Klimek. Together with him, scientists from various research areas and institutions will work on data-driven analytical tools to safeguard supply chains and ensure supply security, incorporating both economic policy and business perspectives.

The aim is to provide decision-makers with solid information for achieving the Austrian and European goals of secure value creation and climate neutrality,” says Klimek. The project is being implemented in close collaboration between the founding organizations (Complexity Science Hub, Logistikum of the Upper Austrian University of Applied Sciences, Verein Netzwerk Logistik (VNL) and WIFO).

The supply chain issue is associated with overarching risks (e.g., geopolitical disagreements, trade wars), as well as with the impact of climate change, requiring new production structures and business models.

At a glance, ASCII’s goals are:

  • Development of a comprehensive data system on supply chains and production networks
  • Monitoring of strategic goods so that interventions can be made at an early stage to avoid supply bottlenecks
  • Generating direct and practice-oriented knowledge about value networks — their change processes and their integration into the macroeconomic context
  • International orientation and broad communication for time-critical and complex issues related to value chains
  • Deriving data-based recommendations for decision-makers

The project is supported by the Austrian Federal Ministry of Labor and Economics (BMAW) with 7.5 million euros, as well as the state of Upper Austria with 2.5 million euros.