Studying human behavior in virtual environments provides extraordinary opportunities for a quantitative analysis of social phenomena with levels of accuracy that approach those of the natural sciences. ASA researchers employed concepts from physics to social science to analyze the “big data” of a multi-player online game. Similarly to the interactions between particles, the notions of “force”, “acceleration”, and “interaction potential” can be used to describe interactions between people in the virtual world, and can be quantified by recording the number of messages or amount of goods, etc., which people exchange . In a case study of this, ASA researchers analyzed the dynamical features of sequences of actions of players depending on players’ traits (age, gender, etc.); finding that while men and women act similarly when performing cooperative actions, women are slightly faster for non-cooperative actions .
Big data analysis can also be used effectively to explore public health issues. For example, ASA researchers used a national data sample of almost two million diabetic patients to show that these individuals have a higher risk of Parkinson’s disease, depression, and schizophrenia. Hypertension was found to be highly sex-sensitive for this group: women were at lower risk during the fertile ages, but higher risk otherwise . These results may be useful to improve screening practices in the general population and this and other possible ways of using big data analysis are being discussed with the decision makers in the Austrian health care system.
 Thurner S & Fuchs B (2015). Physical forces between humans and how humans attract and repel each other based on their social interactions in an online world. PLoS ONE 10(7):e0133185
 Mryglod O, Fuchs B, Szell M, Holovatch Y & Thurner S (2015). Interevent time distributions of human multi-level activity in a virtual world. Physica A: Statistical Mechanics and its Applications 419:681-690.
 Klimek P, Kautzky-Willer A, Chmiel A, Schiller-Fruhwirth I & Thurner S (2015). Quantification of diabetes comorbidity risks across life using nation-wide big claims data. PLoS Computational Biology 11(4): e1004125
Section for Science of Complex Systems, Medical University of Vienna, Austria
Institute for Condensed Matter Physics, National Acad. Sci. of Ukraine, Lviv, Ukraine
Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, USA
Gender Medicine Unit, Medical University of Vienna, Austria
Main Association of Austrian Social Security Institutions, Vienna, Austria
Last edited: 16 March 2016
International Institute for Applied Systems Analysis (IIASA)
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