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Inference in Complex Social Systems Insights and Applications from the Behavior of the Aggregate Nathan Eagle Research Scientist / Postdoctoral Fellow / Fulbright Lecturer Massachusetts Institute of Technology / The Santa Fe Institute / U of Nairobi et al. Fastest Technology Adoption in Human History 1.15B Phones Purchased in 2007 1.9T Text Messages sent in 2007 Processing Power A New Era of Wearable Computing… Data to Science to Engineering Data Science Engineering Behavioral Data in the 21st Century 12 vs. 250 million people… Stop-Watch vs. Computational Social Science 1930s Roethlisberger, F.J., and Dickson, W.J. (1939), Management and the Worker, Cambridge, MA: Harvard University Press. 2008 Communication Networks of 250M people, 12B edges,… Behavioral Data in the 21st Century 400,000 continuous hours… Eagle, N. “Machine Perception and Learning of Complex Social Systems", PhD Thesis, Massachusetts Institute of Technology. 2005. Reality Mining Data 100 Nokia 6600s with Context logging software Location: Celltower ID / User-Defined Names date, area, cell, network, name Bluetooth: Proximate Bluetooth Devices every 5 minutes date, MAC, device name, device type Communication: Phone Call/Text Log date, text/call, incoming/outgoing, duration, number Total Data Over 400,000 hours continuous human behavior data collected over the 2004-2005 academic year. Mika Raento, Antti Oulasvirta, and Nathan Eagle. “Smartphones: an emerging tool for social scientists”, Sociological Methods and Research (in submission). BlueDar : Bluetooth Radar Currently Deployed around MIT Infinite Corridor, Media Lab, Muddy Charles Pub, Sloan Business School, Student Center, … Coming Soon... Cafeterias Elevators Gym ... Nathan Eagle, “Can Serendipity Be Planned?”, MIT Sloan Management Review, Vol. 46, No. 1, pp 10-14, 2004. Outline: Science Data Science Engineering Bias in Self Report Data Bias in Self Report Data: Friendships Bias in Self Report Data: Recency High-Level Situation Classification Probabilistic Graphical Models for Data Filtering Conditioned HMM Q Home,Work , Elsewhere, No Signal Ii Q1 , Q2 , Q3 ,...Qn The Entropy of Life Shannon Information Entropy Applied to Everyday Life Estimate of the amount of structure / randomness in a subject’s routine. n H ( I ) p ( j ) log 2 p( j ) j 1 Low Entropy Subject, I1 High Entropy Subject, I2 H ( I1 ) 30.9 H ( I 2 ) 48.5 Behavioral Entropy Which demographic is the most entropic? Which demographic is the most “infectious”? Eigenbehaviors: Transformation 1 N N i 1 i i i A 1 ; 2 ; 3 ;...N 2 1 N k uk i N i 1 Turk, M., and Pentland, A., "Eigenfaces for Recognition", J. of Cognitive Neuroscience. Vol 3, Number 1., (1991) 71-86 Individual Behavior Space u2 2 4 5 3 6 1 1 7 u1 Eigenbehaviors: Behavior Space u1 Daily human behavior can be clustered in a low dimensional space. u2 Nathan Eagle and Alex Pentland. “Eigenbehaviors: Identifying Structure in Routine”, Proc. Roy. Soc. (in submission). Eigenbehaviors: Affiliation Inference u j2 Can we determine a subject’s affiliation based on short samples of behavior? j I j i 1 i j uij j b M' 2 j j j 2 b I3 I4 j3 u j2 j I2 4 I1 j1 j2 I2 u j1 Group j Behavior Space Friendship vs. Proximity Networks Self-Report Friendship 1-Day Proximity Relationship Inference Is it possible to infer friendship based on proximity behavior? Nathan Eagle and A. Pentland, “Reality Mining: Sensing Complex Social Systems”, Journal of Personal and Ubiquitous Computing, Vol. 10 (4), February 2006. Dyadic Variables Factor Analysis: “In-Role” v. “Extra-Role” Table S5: Loadings from a Factor Analysis for Friendship Variable Name Specific Factor1: ‘InVariance Role’ Communication Proximity at Work 0.2064 0.9194 Number of Unique Locations 0.1171 0.9927 Proximity with no Reception 0.4749 0.6697 Proximity Outside Work 0.6288 0.3535 Proximity at Home 0.7694 0.0716 Proximity on Saturday Nights 0.6689 -0.1584 Phone Communication 0.6476 -0.1418 Factor 2: ‘ExtraRole’ Communication -0.0595 -0.1162 0.0990 0.3491 0.4401 0.6387 0.6523 Inferred v. Reported Network Nathan Eagle, Alex Pentland, and David Lazer. “Inferring Social Network Structure using Mobile Phone Data”, PNAS (in submission). Team Proximity Networks Bi(tj) 1 0 if vertices i and j are ever connected between time t and t otherwise 1 hour Network Evolution Can mobile phone usage reflect an emphasis on ‘networking’ and social network evolution? Organizational Rhythms How the deadlines of an institution can be seen in the collective behavior of its individual members. 168 hrs (7 days) 24 hrs Outline: Engineering Data Science Engineering One Day in the Life... Automatic Diary Generation: A life log from cell tower IDs Life Inferences Class: Sleeping? Class: Lunch? Location: {Home} Phone status: {idle/charging} Time: {late night / early morning} Alarm Clock: {interval} Location: {!= office} People: {lunch crowd={Mike, Push, Martin}} Time: {lunchtime} Class: Partying? Location: {hang outs ={b-side, sevens, BHP}} People: {party friends={Mike, Jon, Aisling}} Time: {evening / late night} Life Query AutoDiary How much sleep did I get last week? When was the last time I had lunch with Josh? How much time did I spend driving when I was last in Mountain View? Where did I go after leaving Marvin’s house last week? Prediction What are the chances of seeing Mike in the next hour? How likely is it that Caroline will call me tonight? Will I be in lab this weekend? Automatic Diary MetroSpark Nathan Eagle and A. Pentland, “Mobile Matchmaking: Proximity Sensing and Cuing”, IEEE Pervasive Computing, 4 (2): 28-34, 2005. Nathan Eagle and Alex Pentland, "Combined short range radio network and cellular telephone network for interpersonal communications." U.S. Patent Application Serial No. 60/568,482. Filed May 6, 2004. MIT ID: 10705T. Assignee: Massachusetts Institute of Technology. Talk Takeaways http://reality.media.mit.edu Behavior Prediction Relationship Inference Computational Social Science Questions? Nathan Eagle [email protected]