Predictability: From Physical to Data Sciences

Saturday, February 16, 2013: 8:30 AM-11:30 AM
Room 204 (Hynes Convention Center)
There is a newfound converge between physical and data sciences. The large amount of raw data that society and technology is generating and collecting, combined with the predictive tools of physical sciences, offers unparalleled predictive understanding of social phenomena, affecting domains of inquiry that could not be quantified in the past. The availability of data has lead to the emergence of several new research fields as the boundary of physical and other sciences, resulting in revolutionary advances in understanding complex networks, human mobility, and human dynamics. The tools generated by these are fueling the emergence of network science, computational social science, and digital humanities. This symposium will present how the tools of physical sciences aid our understanding of complex socioeconomic and technical systems. In the spirit of Wigner, we will explore the unreasonable effectiveness of the quantitative tools of natural sciences in social and engineering domains, bringing experts that apply these in various fields outside of physics. In contrast to data mining approaches, which are prevalent in the big data domain, here we focus on uncovering the mechanism and explaining collective phenomena using the predictive tools of natural sciences.
Albert-Laszlo Barabasi, Northeastern University
Dirk Helbing, Swiss Federal Institute of Technology
Towards Simulating the Foundations of Society
Chaoming Song, Northeastern University
Limits of Predictability in Human Mobility
Marta Gonzalez, Massachusetts Institute of Technology
Understanding Road Usage Patterns in Urban Areas
Dirk Brockmann, Northwestern University
Are Pandemics Predictable?
Boleslaw Szymanski, Rensselaer Polytechnic Institute
On the Influence of Committed Minorities on Social Consensus
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