Dirk Helbing

Dirk Helbing

Professor of Computational Social Science, ETH Zurich

Dirk Helbing is a physicist and Professor of Computational Social Science internationally known for his work on pedestrian crowds, vehicle traffic, and agent- based models of social systems. Furthermore, he coordinates the FuturICT Initiative, which focuses on the understanding of techno-socio-economic systems using smart data.  His team is engaged in establishing the core of a so-called Planetary Nervous System - an open, transparent and participatory information system to support real-time measurements of our world.


BREAKING THE WALL TO DIGITAL DEMOCRACY. How Socio-Physics Shapes the Future of Smart Societies

As the development of the Internet of Things is taking up speed, connected devices are producing staggering amounts of data. Estimates say that by 2020, there will be 26 times more connected things than people – devices which will produce 400 zettabytes of data per year (one zettabyte is a trillion gigabytes). Managing this flood of data is one of the biggest challenges facing policy, industry and civilian societies. The task of scientists is to test and propose rules, frameworks and technologies to support this process, reveal opportunities and prevent risks and abuse. Dirk Helbing is a physicist and professor of computational social science with a particular interest in modelling and simulating complex socio-economic systems and scenarios. With his team at ETH Zurich, he is researching how big data from connected devices can be fed into a so-called Planetary Nervous System, a transparent, open-access information system which can support real-time measurements of the world. A system like this could revolutionise many sectors, from urban planning and traffic control to the early detection of epidemics and earthquake prediction. In the wrong hands, however, big data can pose enormous risks to privacy and personal freedom. As opposed to corporate or state-owned data mining tools, Dirk proposes a citizen-owned participatory platform, with extensive features to protect users’ privacy and the ultimate goal to treat big data – and the information extracted from it – largely as public goods. At Falling Walls, he presents this model of a democratic data ecosystem as an alternative to gloomier “Big Brother” scenarios.