PhD defence w/ Ulf Aslak

Complexity in Social Data

Towards mapping and understanding complex phenomena in big social systems, using data science

Abstract

It is now possible to accurately measure human behavior and understand it at large scales. Smartphones, social media sites and markets deliver a massive stream of data, that can be tapped into to understand previously unknown social phenomena. One of the things we are discovering is that human social systems are highly complex, displaying many of the hallmarks of complex systems, such as large scale self-organization and reoccurring patterns. At the same time, they are extremely chaotic, making it near impossible to accurately simulate or predict their behavior. Computational social science, or social data science, has therefore emerged as an interdisciplinary field of social scientists turned data scientists and vice versa, with the ambition to answer fundamental questions about human behavior. Operating within this field, this thesis explores complex phenomena in social and behavioral data. It spans a wide range of topics within different fields, from neuroscience to animal ecology, but is connected throughout by the idea of complexity.

Contact the information at the Department of Economics (room 26.0.20) before the PhD defence in order to obtain a copy of the PhD thesis.