Particular research agendas will follow the interests of the researchers at the Hub. The main research areas will include
Health care and medicine
Every medical treatment filed in the Austrian social security system is registered electronically. For the entire Austrian population this represents approximately 100 million invoices per year. This is a big data challenge. By solving it, the health status of the entire population can be continuously monitored. For millions of patients it will be possible to detect which medical problems occur in combination, which combination of drugs particularly often leads to further complications, and which preventive measures appear to be particularly suitable and effective. Subsequently, it might become possible – based on complications that an individual already has – to indicate the risk potential to get certain complications in the future. This allows for personalized prevention. Furthermore, big health care data enables the mapping of the entire health care system in greater detail; its strengths and weaknesses become visible in an objective manner. Objective data-driven facts are essential for a financially sustainable and viable functioning of the health care system.
Industry, production, and the internet of things
Urban development and smart cities
The proportion of the world population living in cities is rapidly increasing. This entails not only challenges for the infrastructure, but requires answers to fundamental questions: How do cities work? Do people interact differently when they live together in a confined space? Does a dense environment affect their efficiency, health, communication patterns, roads, or collective opinion-forming? There is evidence that the size of cities significantly affects these factors. Hence, how should cities be planned and built in an intelligent way? How can the immense data streams a city is constantly producing be aggregated in a useful way, and then used to develop the city (as a collective of social networks) in a targeted, sustainable and resource-friendly way?
Systemic risks in societies and economies
Public liabilities due to bank bailouts may reach enormous dimensions. The systemic risk an individual bank carries in case of bankruptcy is extremely difficult to estimate and predict. Today, each financial transaction is registered electronically. The amount of transactions represents another big data challenge. With this information, the systemic risk posed by all banks of a country or region can be calculated. It is possible to identify systemically dangerous actors, and to take targeted action to prevent failure. The combination of complexity science and big data can help to create a more secure financial system.
Quantitative computational social science, opinion formation, e-governance
How can citizens be encouraged to competently and actively participate in local decision-making processes (eg where to build a roundabout or install a traffic light)? With complexity science methods, users can, for example, simulate traffic conditions online; they can immediately learn the consequences of decisions in a playful way. Thus, big data and complexity science can help to establish a factual basis for decisions, and to understand the difficulties, interdependencies, and ambiguities in complex systems, such as traffic.
Fundamental understanding of complex systems