"Computational Social Science"

From David Lazer, Alex Pentland, Lada Adamic, Sinan Aral, Albert-László Barabási, Devon Brewer, Nicholas Christakis, Noshir Contractor, James Fowler, Myron Gutmann, Tony Jebara, Gary King, Michael Macy, Deb Roy, Marshall Van Alstyne:

To date, research on human interactions has relied mainly on one-time, self-reported data on relationships. New technologies, such as video surveillance (1), e-mail, and “smart” name badges, offer a moment-by-moment picture of interactions over extended periods of time, providing information about both the structure and content of relationships. For example, group interactions could be examined through e-mail data, and questions about the temporal dynamics of human communications could be addressed: Do work groups reach a stasis with little change, or do they dramatically change over time (2)? What interaction patterns predict highly productive groups and individuals? Can the diversity of news and content we receive predict our power or performance (3)? Face-to-face group interactions could be assessed over time with “sociometers.” Such electronic devices could be worn to capture physical proximity, location, movement, and other facets of individual behavior and collective interactions.

The data could raise interesting questions about, for example, patterns of proximity and communication within an organization, and flow patterns associated with high individual and group performance (4). We can also learn what a “macro” social network of society looks like (5), and how it evolves over time. Phone companies have records of call patterns among their customers extending over multiple years, and e- Commerce portals such as Google and Yahoo collect instant messaging data on global communication. Do these data paint a comprehensive picture of societal-level communication patterns? In what ways do these interactions affect economic productivity or public health? It is also increasingly easy to track the movements of people (6). Mobile phones allow the large-scale tracing of people’s movements and physical proximities over time (7). Such data may provide useful epidemiological insights: How might a pathogen, such as influenza, driven by physical proximity, spread through a population?

Available here. To model that data, you need a theory of highly integrated, complex dynamical systems. That set of theory is what I refer to as "systems theory" on this blog.

Computational social science is actually not new. Csikszentmihalyi started collecting such data with pagers decades ago using his "Experience Sampling Method" (ESM).

Via Dan Katz at the Computational Legal Studies blog.