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. 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. Thesis Supervisor: Alex P. Pentland Title: Toshiba Professor of Media Arts and Sciences, MIT. 2 Machine Perception and Learning of Complex Social Systems by Nathan Eagle Thesis Committee: Advisor: . Alex (Sandy) Pentland Toshiba Professor of Media Arts and Sciences Massachusetts Institute of Technology Thesis Reader . Pattie Maes Professor of Media Arts and Sciences Massachusetts Institute of Technology Thesis Reader . J. Richard Hackman Professor of Social and Organizational Psychology Harvard University 3 Acknowledgments This thesis is the product of many collaborations. My primary collaborator over the last few years has been Alex (Sandy) Pentland. I feel lucky to have landed in his group four years ago and am looking forward to continuing our collaborations. Additionally, I would like to acknowledge the rest of Sandy’s Human Dynamics group at the MIT Media Lab: Juan Carlos Barahona, Joost Bonsen, Ron Caneel, Wen Dong, Jon Gips, Anmol Madan, and Michael Sung – as well as former members including Martin Martin, Rich DeVaul, Brian Clarkson, Sumit Basu and Tanzeem Choudhery. While it has been a diverse and evolving group, it has remained unified around our principle research direction – modeling human dynamics in such a way as to build applications that better support both the individual and the group. My many collaborators deserve particular acknowledgement. Mika Raento has been the principal architect of the software we used to collect the Reality Mining dataset. Tony Pryor, Greg Sterndale and Pedro Yip have been the driving forces behind the development of Serendipity. David Lazer, a Political Science professor at Harvard University, has been a wonderful social science resource to bounce ideas off of. Professor Judith Donath has also been instrumental in my thinking about the privacy implications of this type of research. Other faculty members that have shaped my thinking about this research direction include Tom Allen, Pattie Maes, and Richard Hackman. Aaron Clauset and Leon Dannon have been two great physicist friends and collaborators on complex network analysis. Push Singh has been a wonderful idea generator and influenced my thinking in areas from artificial intelligence to social relationships. Caroline Buckee has proven to be one of the best science editors and writers I have worked with – as well as someone who shares my enthusiasm for entropy. Stephen Guerin of the Red Fish Group is responsible for the wonderful visualizations of this dataset, and Mike Lambert has been responsible for designing our diary application. I’d also like to acknowledge Max Van Kleek, Bo Morgan, Lauren Oldja, and Sanith Wijesinghe. This research would not be possible without the generous donations from Nokia. In particular Harri Pennanen, Kai Mustonen, Hartti Suomela, Saku Hieta, Kari Pulli, Peter Wakim, Franklin Reynolds, Suvi Hiltunen, Ken Beausang, and Timo Salomaki have all been instrumental in the research and have certainly played a large role as a Media Lab sponsor. 4 Many people who work at the Media Lab have also played a great role in shaping and supporting this research direction. The Media Lab administrators (both past and present) are certainly deserving of recognition: Elizabeth Hartnett, Mary Heckbert, Linda Peterson, and Pat Solakoff. There are simply too many Media Lab friends to acknowledge, but a partial list would include: Aisling Kelliher, Andrea Lockerd Thomaz, Andrew Sempere, Anindita Basu, Ashish Kapoor, Barbara Barry, Ben Vigoda, Brad Lassey, Brian Chow, Cameron Marlow, Cory Kidd, Dave Merrill, Geva Patz, Jeana Frost, Joannie DiMicco, Josh Lifton, Lily Shirvanee, Mat Laibowitz, Matt Reynolds, Michael Rosenblatt, Saul Griffith, Stacie Slotnick, and Win Burleson. Final acknowledgement should go out to both my family, and of course, the intrepid group of Reality Mining subjects. 5 Table of Contents ABSTRACT .................................................................................................................................................. 2 ACKNOWLEDGMENTS............................................................................................................................ 4 TABLE OF CONTENTS............................................................................................................................. 6 LIST OF FIGURES.................................................................................................................................... 11 PREAMBLE ............................................................................................................................................... 18 CHAPTER 1 INTRODUCTION ......................................................................................................... 20 1.1 TRADE-OFFS IN TRADITIONAL SOCIAL DATA GATHERING ........................................................... 20 1.2 NEW INSTRUMENTS FOR BEHAVIORAL DATA COLLECTION ....................................................... 22 1.3 CONTRIBUTIONS......................................................................................................................... 22 1.4 THESIS ROADMAP ...................................................................................................................... 23 CHAPTER 2 BACKGROUND ............................................................................................................ 26 2.1 COMPLEX SOCIAL SYSTEMS....................................................................................................... 27 2.2 COMPLEX NETWORKS ................................................................................................................ 27 2.3 SOCIAL PROXIMITY SENSING ..................................................................................................... 29 2.3.1 Technology Overview ........................................................................................................... 29 2.3.2 Reality Mining as a Proximity Sensing Technology ............................................................. 32 2.4 SOCIAL SOFTWARE .................................................................................................................... 32 2.4.1 The Opportunity for Mobile Social Software........................................................................ 33 2.5 SHORTCOMINGS IN SOCIAL SCIENCE .......................................................................................... 34 2.5.1 Reliance on Self-Report Measures........................................................................................ 34 2.5.2 Absence of Longitudinal Data .............................................................................................. 35 6 2.5.3 Study of macro-networks ...................................................................................................... 36 CHAPTER 3 METHODOLOGY & RESEARCH DESIGN............................................................. 38 3.1 HUMAN SUBJECTS APPROVAL.................................................................................................... 38 3.2 PARTICIPANTS ............................................................................................................................ 39 3.2.1 Recruitment..........................................................................................................................