Researchers will one day be able to accurately predict such things as the economic and social effects of billions of new Internet users in China and India, or the exact location and number of airline flights to cancel around the world in order to halt the spread of a pandemic, says Indiana University’s Alessandro Vespignani. This capability will be possible thanks to “reality mining”, which involves the collection of data from machine-sensed sources to provide knowledge about aggregated human behavior.

In much the same way that Bluetooth, Global Positioning Systems and WiFi leave behind detailed traces of our lives, the proliferation of sensors and tags generating data at micro, one-to-one interaction levels should provide researchers and scientists with a wealth of data to examine to help accurately forecast the effects of phenomena like catastrophic events, mass population movements or invasions of new organisms into ecosystems.

Researchers have already shown they can track the movement of as many as 100,000 people at a time over six months using mobile phone data, and that worldwide currency traffic can be used as a proxy for human mobility.

In the aftermath of the September 11 attacks the Homeland Security Dept. turned to Microsoft spin-off Inrix for an indication of what might happen if terrorists took down the Golden Gate Bridge. Through reality mining of data from satellite navigation gear that is widely installed on trucks and some cars to produce real-time traffic information, Inrix was able to predict that the Bay Area would pull off an amazingly quick recovery as drivers adapted to the new circumstances.

In his article "Predicting the Behavior of Techno-Social Systems," Vespignani recognizes there will be challenges in creating a predictive system that includes social adaptation. Large-scale data, for example, is still needed about how information spreads and society reacts in times of crisis, but he believes advancing communications databases may address that issue.

"While the needed integrated approach is still in its infancy, using network theory, mathematical biology, statistics, computer science and nonequilibrium statistical physics will play a key role in the creation of computational forecasting infrastructures," Vespignani said. "And that should help us design better energy distribution systems, plan for traffic-free cities and manage the deployment of the world's resources."

Proponents of reality mining, such as Vespignani, highlight its potential for good, but privacy advocates understandably have concerns about the technology. Marketers are positively salivating at the potential to target messages to individuals based on highly specific information.

Already cellular operators have begun signing deals with business partners who are eager to market products based on specific phone users' location and calling habits. Imagine how valuable phone company records will become as more and more people use their mobile phones to browse the Internet, purchase products and update their Facebook pages. And mobile phone records are only the tip of the iceberg in terms of future data sources, with technology such as RFID tags quickly gaining in popularity.

As is often the case, technology is once again a double-edged sword - and once again it is personal privacy that is under threat. There’s no doubt that reality mining has the potential to benefit society in a myriad of ways. But steps need to be put in place to ensure that those benefits don’t come at the expense of the individual’s right to privacy. Alessandro Vespignani’s article, "Predicting the Behavior of Techno-Social Systems," appears in the Perspectives section of Science.