SPAC uses large scale computational tools to study societal challenges, especially in disease forecasting, human behavior and public policy. This multidisciplinary research group takes advantage of the so-called “Big-Data Revolution” and works together to understand how individual behaviour impacts on society. We also focus on the risks that these technologies might entail and we help establish the guidelines for ethical uses of data science and artificial intelligence. The European Research Council has awarded  a Starting Grant to the group PI to conduct the research project “Fake News and Real People – Using Big Data to Understand Human Behaviour (FARE)”.

Understanding complexity has always been a hallmark of physics research and, through theory, experiments, and models, physicists have made fundamental contributions to many different complex fields. Right now, the so-called Digital Revolution is offering radically new ways to study complex behaviours and this is being recognized by physics and computer science departments in many top universities worldwide. Complexity Science (CS) studies complex systems and tries to identify general principles. Complex systems consist of a large number of interacting heterogeneous components (parts, agents, humans etc.), resulting in highly non-linear and unpredictable behaviour, with emergence properties. CS theory typically builds on statistical physics and dynamical systems, but also on information theory and, increasingly, network science.

The combination of large-scale data sources and a growing toolbox from machine learning and big data analytics, is making it easier to extract patterns and offer some predictions. In fact, many of the methods developed by statistical and particle physics are now being applied to societies and there is a growing perception that physics will be fundamental to study sociology and even psychology. Leading scientists are calling this new science “Social Physics” and arguing that, in some ways, complexity science will study the physics of human interactions.

Group webpage at LIP