Since 2002, the Massachusetts Institute of Technology (MIT) has published the annual Hitlist of ten technologies that, according to scientists, will change the world.

"10 Breakthrough Technologies" is the title of the MIT's 2021 list of ten potentially world-changing innovations. This time the focus was on: Alternatives to passwords, analysis of different virus mutations, large capacity batteries, artificial intelligence for protein analysis, malaria vaccines, energy-efficient cryptocurrency transactions, pills against covid, practical fusion reactors, computer-generated data for machine learning and a carbon disposal plant.

Some technologies prove to be just bubbles, others exceed all expectations eliciting challenging societal debates such as the current ones about energy transition technologies, e-mobility or Industry 4.0. However, one issue is beyond all question:

Technology changes society and is - vice versa - shaped by societal institutions and organisations. If technologies are connected to contexts of application, utilisation, deployment and exploitation, an additional social dynamic is created. This step, which we call innovation, can target either new commercial products and processes (technological innovation) or new organisational structures (social innovation).

New technologies and innovations have the potential to redraw the image of our society in a completely new way. In doing so, they also challenge the institutions concerned with societal planning, policymaking and coordination because as future objects they are neither predictable nor accessible.

This is precisely where the Sociology of Technology and Innovation team engages in research, teaching and knowledge transfer. Analysing societal phenomena around the genesis, structures and consequences of technologies and innovations, enables us to understand, describe and explain the complex dynamics in technology and innovation.

The complexity of our research area requires an innovative and special methodological repertoire including social simulation methods from Computational Social Science. By modelling social phenomena based on theoretical considerations and empirical data, these methods enable us to draw conclusions on current and future social dynamics and project different future scenarios.