Machine perception and learning of complex social systems
Name
61896628-MIT.pdf
Description
Full printable version
Size
16.51 MB
Format
Adobe PDF
Checksum (MD5)
c1e141a5b12fd1fedc52816990d12742
Author(s)
Eagle, Nathan Norfleet
Advisor(s)
Alex P. Pentland.
Date Issued
2005
Publisher
Massachusetts Institute of Technology
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 has generated one of the largest datasets of continuous human behavior ever collected, representing over 300,000 hours of daily activity. In this thesis we describe how this data can be used to uncover regular rules and structure in behavior of both individuals and organizations, infer relationships between subjects, verify self- report survey data, and study social network dynamics. By combining theoretical models with rich and systematic measurements, we show it is possible to gain insight into the underlying behavior of complex social systems.
Description
Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.
Includes bibliographical references (p. 125-136).
Subjects
Architecture. Program In Media Arts and Sciences
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
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