Clever social scientists are coming up with new ways to put the burgeoning amounts of data on social networks to work for societies they are supposed to reflect. By employing the help of inquisitive physicists these psychohistorians are analyzing patterns in the network dynamics we’ve discussed in general models, and refining models to study at far more precise levels. A researcher at Northeastern University has managed to devise an algorithm that analyzes a person’s mobile-phone records and then with 93% accuracy predict where that person will be at any point during the day. In another interesting application Dr. Vesipigani along with his team have created a program named GLEAM (Global Epidemic and Mobility Model) which splinters the world into hundreds of thousands of squares. Using these squares as hubs the program models travel patterns between other squares (busy roads, flight paths, etc.,) which demonstrated potential in 2009 when it successfully mimicked an outbreak of a strain of influenza known as H1N1 as it made its way across different countries with commendable accuracy. Within the guts of these models there are apparent network components such as transiency and network flows at work. The idea of using the rich new sources of data from mobile technology to model social network dynamics aspires to achieve such lofty goals as to predict potential tipping points where riots might break out, or model the breakdowns of trust between banks and customers that cause financial crises. I myself am interested to see how well we could use similar social network analysis to predict the business cycles of industry and performances within the market, perhaps measures of a firms social network could be revealing of sudden upticks in its market value.
Any thoughts are very welcome.