Chair: Dr. Ciro Cattuto, ISI Turin
The approach to monitoring, measuring, and dealing with collective phenomena in global systems has been rapidly evolving under the pressure of three main drivers: 1) the end of linear thinking brought forth by the maturity of complex system science applied to socio-technical systems; 2) the ability to monitor, quantify and model human behaviors at unprecedented levels of resolution and scale, unleashed by the planetary-scale adoption of the World Wide Web, mobile communication technologies, e-commerce systems, and on-line social networks; 3) the emergence of new forms of human-machine compositionality arising from the designed or emergent interplay of ICT services and communities of citizens.
These innovations are just starting to display their full transformative power. Historically speaking, the current level of interconnectedness and digital visibility is a sudden event with no precedents, and its inception is forcing change in the way organizations think about global systems and deal with global phenomena, both in the public and in the private sectors.
This session will discuss major fundamental challenges in realizing the vision of learning actionable models of social processes from big data sources on socio-technical systems, covering measurement, modeling and learning from data, and future human-machine compositional patterns.
- Big Data Challenges and Models
- Economic Complexity and Financial Networks
- Algorithmic and Statistical Challenges of Big Data for GSS
- New data for old questions