Organizational rhythms - the search for the patterns of the aggregate
Nathan Eagle & Alex (Sandy) Pentland

Dr Nathan Eagle is a Visiting Scientist at the MIT Media Lab, where he recently completed his PhD under Professor Alex (Sandy) Pentland. Professor Pentland is the Toshiba Professor of Media Arts and Sciences at the MIT Media Laboratory and Director of the Human Dynamics research group. Their research focuses on projects that span a variety of disciplines from appropriate technology to artificial intelligence. This contribution to receiver comes from Eagle's dissertation on machine perception and learning of complex social systems, which explores the intersections of mobile phones, machine learning, and organizational behavior.
 
 
 
Today's mobile phones are capable of sensing the world around them. Using information such as cell tower IDs or proximate Bluetooth devices, it is possible to get a depiction of an individual's current context. When this data is captured over extended periods of time, it can be used to generate a predictive model of the user's life. And if we extend this further, capturing mobile phone data across the individuals within an organization can give us unprecedented insight into the large-scale dynamics of collective human behavior. Furthermore, a dataset providing the proximity patterns and relationships within large groups of people has implications within the computational epidemiology communities, and may help build more accurate models of airborne pathogen dissemination, as well as other more innocuous contagions, such as the flow of information around the water cooler.

In the Reality Mining project, we distributed 100 context-logging phones to people working at MIT and collected almost 500,000 hours of continuous human behavioral data. We showed that Bluetooth-enabled mobile phones can be used to discover a great deal about the user's context and relationships. In this paper we will focus on extending this base of user modeling to explore modeling complex social systems. We will provide several illustrative examples of how this data can be used to learn more about both team and organizational dynamics.

Team dynamics
By continuously scanning for Bluetooth devices and logging the people proximate to an individual, we are able to quantify a variety of properties about the individual's work group. Although most research in networks assumes a static topology, proximity network data is extremely dynamic and sparse. We will compare aggregate statistics between two different research groups at the Media Lab in an attempt to gain insight into fundamental characteristics of the research groups themselves.

While each research group at the Media Lab is centralized around a faculty director, the proximity networks are not reflective of this static organizational structure. In many instances, the proximity network's degree of distribution is indicative of a hub-and-spoke formation, however the roles that are played within this structure are not static. Individuals that are hubs during one period of time fluidly exchange places with other team members on the periphery of the proximity network. This type of dynamic may be characteristic of the underlying nature of research groups at the Media Lab. As deadlines approach for specific individuals, they begin to spend more time in the Media Lab and increasingly rely on support from the rest of the group. Upon completion of a project, they resume their normal routines and can provide similar support to others. This pattern of behavior has been shown to vanish when the entire group (or organization) is working towards the same deadline.
 
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