“We understand the universe much better than we understand our own societies” said Professor Helbing, Chair of Sociology, Swiss Federal Institute of Technology, at this year’s annual meeting of the American Association for the Advancement of Science (AAAS).
Dirk Helbing was speaking at a session entitled “Predictability: from physical to data sciences”. This was an opportunity for participating scientists to share ways in which they have applied statistical methodologies they usually use in the physical sciences to issues which are more ‘societal’ in nature. Examples stretched from use of Twitter data to accurately predict where a person is at any moment of each day, to use of social network data in identifying the tipping point at which opinions held by a minority of committed individuals influence the majority view (essentially looking at how new social movements develop) through to reducing travel time across an entire road system by analysing mobile phone and GIS (Geographical Information Systems) data.
They are not alone in working in this way. With technological advances, greater interconnectivity and the availability of data in ever larger quantities, subjects across the physical and social sciences are becoming more quantitative and predictive in nature. Working across physical science and social science boundaries is increasingly common, new fields such as ’integrated systems biology’ and ‘computational social science’ reflect this.
The underlying thinking is that if we can model the weather, flight paths and factory production lines then we can model human networks and group behaviour. We can maybe model society itself.
With their eye on the big picture, Dr Helbing and multidisciplinary colleagues are collaborating on FuturICT, a 10-year, 1 billion EUR programme which, starting in 2013, is set to explore social and economic life on earth to create a huge computer simulation intended to simulate the interactions of all aspects of social and physical processes on the planet. This open resource will be available to us all and particularly targeted at policy and decision makers. The simulation will make clear the conditions and mechanisms underpinning systemic instabilities in areas as diverse as finance, security, health, the environment and crime. It is hoped that knowing why and being able to see how global crises and social breakdown happen, will mean that we will be able to prevent or mitigate them.
Modelling so many complex matters will take time but in the future, we should be able to use tools to predict collective social phenomena as confidently as we predict physical phenonema such as the weather now.