Machine Perception and Learning of Complex Social Systems
Machine Perception and Learning of Complex Social Systems by Nathan Norfleet Eagle B.S., Mechanical Engineering, Stanford University 1999 M.S., Management Science and Engineering, Stanford University 2001 M.S., Electrical Engineering, Stanford University 2003 Submitted to the Program of Media Arts and Sciences, School of Architecture and Planning, in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY IN MEDIA ARTS AND SCIENCES at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY May 2005 © Massachusetts Institute of Technology, 2005. All rights reserved. Author . Program in Media Arts and Sciences Month Day, 2005 Certified by . Alex P. Pentland Professor of Electrical Engineering and Computer Science Thesis Supervisor Accepted by . Andrew B. Lippman Chair Departmental Committee on Graduate Students Program in Media Arts and Sciences Machine Perception and Learning of Complex Social Systems by Nathan Norfleet Eagle Submitted to the Program of Media Arts and Sciences, School of Architecture and Planning, on April, 29, 2005, in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Abstract The study of complex social systems has traditionally been an arduous process, involving extensive surveys, interviews, ethnographic studies, or analysis of online behavior. Today, however, it is possible to use the unprecedented amount of information generated by pervasive mobile phones to provide insights into the dynamics of both individual and group behavior. Information such as continuous proximity, location, communication and activity data, has been gathered from the phones of 100 human subjects at MIT. Systematic measurements from these 100 people over the course of eight months have generated one of the largest datasets of continuous human behavior ever collected, representing over 300,000 hours of daily activity.
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