Cell phones afford a convenient platform to advance the understanding of social dynamics and influence, because of their pervasiveness, sensing capabilities, and computational power. Many applications have emerged in recent years in mobile health, mobile banking, location based services, media democracy, and social movements. With these new capabilities, we can potentially identify exact points and times of infection for diseases, determine who most influences us to gain weight or become healthier, know exactly how information flows among employees and how productivity is affected in our work spaces, and understand how rumors spread.
There remain, however, significant challenges to making mobile phones the essential tool for conducting social science research and also support mobile commerce with a solid social science foundation. Perhaps the greatest challenge is the lack of data in the public domain. There is a need for data large and extensive enough to capture the disparate facets of human behavior and interactions. Another major challenge lies in the interdisciplinary nature of conducting social science research with mobile phones. Software engineers need to work collaboratively alongside social scientists and data miners in various fields.
In an attempt to address these challenges, SBP has worked with the MIT Human Dynamics laboratory to release several mobile data sets in “Reality Commons” that contain the dynamics of several communities of about 100 people each. They invite researchers:
- To propose and submit their own applications of the data to demonstrate the scientific and business values of these data sets,
- To suggest how to meaningfully extend these experiments to larger populations, or
- To develop the math that fits agent-based models or systems dynamics models to larger populations. The problem itself will be open-ended and encourage approaches from different disciplines, encompassing a range of applications using this data, including:
- Social network analysis
- Data visualization
- Simulation studies
- Predictive modeling
- Qualitative studies to supplement existing quantitative work
- Creative new applications of the data