Social Physics and the Data Driven Society

Social Physics and the Data Driven Society Prof. Alex Pentland, MIT Connection Science and Engineering WEF Big Data, Hyperconnected World Outline 1. Big Data and computational social science 2. Distributed Intelligence 3. Network Intelligence 4. Big Data breaks science Copyright alex pentland 2011 1. Understanding Ourselves: The Big Data Revolution Human Dynamics Observatories: (1) MIT Reality Mining Study (2) MIT Social Evolution, (3) MIT Friends and Family (Current), (4) MIT lifelog pioneers; MyLifeBits, (5) Sociometric Badge studies, (6) Midwest Field Station , (7) Framingham Heart Study, (8) Large Call Record Datasets , (9) “Omniscient”/All-Seeing View Ahrony, Pentland Background: Humans Have Two Types of Thought Nobel Prize winner Kahneman, father of behavioral economics Fast Parallel Automatic Associative Slow Serial Controlled Rule-based Habitual (System 1) Attentive (system 2) Conceptual Representations Past, Present, Future Can be evoked by language Content Process Social Physics Copyright alex pentland 2012, all rights reserved People Mostly Learn by Examples, not Arguments or Reasoning 90% - 10% balance Rendell et al, Social Learning, Science 4/10 Copyright alex pentland 2012 all rights reserved 6 Influence Model & Idea Flow s1 s2 s1 0.8 0.2 s2 0.2 0.8 Internal State s1 Internal State s2 Internal State s2 Private Observation Copyright alex pentland 2012, all rights reserved 7 Using sensors in smart phones to obtain different type of social networks. Call Network Proximity Network Friendship Network Co-location Network Copyright alex pentland 2012, all rights reserved 45% accuracy predicting app downloads. Gompertz function describes influence Social Exposure Predicts Behavior Pan, Aharony, Pentland 65 young families, 12 months data 8 process id process id (c) 1 2 3 4 5 6 1 2 3 4 5 6 Inverse Problem: Discovery of Influence, Node State Raw Observations From Nodes clean state estimate for nodes influence structure connecting nodes Copyright alex pentland 2012 all rights reserved Understanding Ourselves: Behavioral Demographics Copyright alex pentland 2012 90 million people continuously Accuracy 4 times normal demographics Copyright 2012 Alex Pentland Patterns of Health With MGH: Phenotypic + Genetic Characterization Alcohol diabetes Copyright 2012 Alex Pentland Patterns of Finance Success Scoring of Unbanked Unlikely to succeed Likely to succeed Including life coaching 2. Distributed Intelligence Shaping By Social Incentives 40th Anniversary of the Internet Grand Challenge Copyright alex pentland 2012 Pickard, Pan, Rahwan,Cebrian,Madan,Crane,Pentland Shaping by Social Incentives incentives that leverage social influence Global externality: tragedy of the commons Localized externality: The peers of individuals A and B receive rewards for behavior of A, B. Mani, Rahwan, Pentland Copyright alex pentland 2012 all rights reserved Behavior Shaping By Social Influence • Reward individuals for their peers' behavior • The total reward distributed to the peers of actor j is less than the Pigouvian subsidies to j if Mani, Rahwan, Pentland Incentive personal utility externality cost peer pressure peer cost Copyright alex pentland 2012 all rights reserved Social Influence incentive mechanism is 3.5 times as efficient as standard incentive mechanism 65 young families, 3 months data Aharony, Pan, Pentland Standard Incentive Social media Incentive Peer reward incentive Copyright alex pentland 2012 all rights reserved 3. Network Intelligence •Learn the underlying hidden influence network from historical data •Use edge weights (of network) to derive adoption potential Influence Model Influence Model : edge weight between i and j in the diffusion network = 1 if j has adopted strategy a, =0 if not. Copyright alex pentland 2012 all rights reserved Altshuler, Pentland •Learn the underlying hidden influence network from historical data •Use edge weights (of network) to derive adoption potential •Calculate behavior predictions Behavior Change Model Behavior Propagation Diffusion model: is the individual susceptibility factor of user u a s p u u u Local u a P E Prob x 1| Ν u 1 e Copyright alex pentland 2012 all rights reserved Altshuler, Pentland •Learn the underlying hidden influence network from historical data •Use edge weights (of network) to derive adoption potential •Calculate behavior predictions •Predict cascade frequency and size, from local influence forces Model 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Trend Penetration Probability Probability of Idea-Behavior Flow, Φ(C) Copyright alex pentland 2012 all rights reserved Social trading: users can see and copy trades of another user Φ(C) and the Wisdom of the Crowd Copyright 2012 Alex Pentland All Rights Reserved Altshuler, Pentland Isolation and Herding User IDs User IDs •2.7 Million users •“Twitter-like” social based financial trading •Trading as collaborative problem solving eToro – Social Trading Network Copyright alex pentland 2012 all rights reserved Altshuler,Pentland Social Trading (Annual ROI) Social Trading Non-social trading 0 50 100 150 200 250 300 350 400 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 Days Cummulative daily ROI Combined, Max drop: -7.81% , ROI: 5.58% , ROI\ STD: 22.25 , Days win ratio: 2.34 Social Trading ROI : 5.58% Max drawdown : -7.81% Sharpe (yearly) : 1.03 Days win ratio : 0.7 Social Intelligence confidential Athena Wisdom 2012` Altshuler,Pentland Stupidity of the Crowd Copyright 2012 Alex Pentland all rights reserved Pan, Alshuler, Pentland “Guru trading” Social Trading Non-social trading 0 50 100 150 200 250 300 350 400 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 Days Cummulative daily ROI Combined, Max drop: -14.49% , ROI: 18.22% , ROI\ STD: 32.56 , Days win ratio: 2.35 “Guru Trading” ROI : 18.22% Max drawdown : -14.49% Sharpe (yearly) : 1.56 Days win ratio : 0.7 Guru Trading (Annual ROI) Experts confidential Athena Wisdom 2012 Altshuler,Pentland Copyright alex pentland 2012 Return on Investment Social vs non -social investors ROI for 2.7 Million Investors, 1 Year Isolated Echo chamber Idea->Behavior Flow, Φ(C) Decision Accuracy Depends on Diversity of Information Sources Altshuler,Pentland Selecting Oracles Confidentiall Athena Wisdom 2012 0 5 0 100 150 200 250 300 350 400 450 500 -50% 0 50% 100% 150% 200% 250% Trading days ROI ROI: 209.4% Sharpe: 6.1 Drawdown: - 4.9% Daily win ratio: 85.3% Altshuler,Pentland Insuring Diversity of Information User IDs User IDs •2.7 Million users •“Twitter-like” social based financial trading •Trading as collaborative problem solving eToro – Social Trading Network Copyright alex pentland 2012 all rights reserved Altshuler,Pentland Tune Network to Optimize Φ(C) Day of the experiment ROI in % (Social – Non-Social) Currency Trading ROI Φ(C) Copyright alex pentland 2012 all rights reserved Altshuler,Pentland Pattern of Social Ties and Φ(C) Engagement: Density of sharing of information within group Exploration: Harvesting New Ideas outside of group; `fat tails’ Copyright alex pentland 2012 all rights reserved Copyright 2012 Alex Pentland All Rights Reserved Exploration and Engagement: a study of white collar workers Engagement in face-to-face accounts for 30% of between-group variation in productivity Exploration in face-to-face accounts for 10% of between-group variation in productivity Wu,Waber,Aral,Brinjolfsson,Pentland Best Research Paper, ICIS 2008 Copyright alex pentland 2012 all rights reserved BAC Call Center Productivity Study Average Call Handle Time Engagement:f Face-to-Face Network Productivity correlated with group engagement Phase 1 Phase 2 Optimize Idea Flow Φ(C) Energy Engagement Average Call Handling Time Copyright alex pentland 2012 all rights reserved Olguin, Waber, Kim, Pentland Changing coffee break structure produced: 30% increase engagement 20% decrease stress $15M / year savings Copyright alex pentland 2012 all rights reserved Copyright 2012 Alex Pentland All Rights Reserved Olguin, Waber, Kim, Gloor, Pentland Φ(C) Measures Are Typically 40% of Performance Harvard Business Review: Breakthrough Idea of the Year Cities and Φ(C) Lieben-Nowell; Krings et al; Engagement: Density of sharing of information within group Exploration: Harvesting New Ideas outside of group Copyright alex pentland 2012 all rights reserved Φ(G) Φ(G) Φ(C) 4. Big Data breaks science Science as practiced assumes strong theoretical understanding Big Data is good for interpolation but not for extrapolation Big Data governance requires thousands of social science experiments Trento, Italy Copyright alex pentland 2012, all rights reserved Data from private companies and Provential Authority Trentino Open Living Data Project (TOLD) Application scenarios:  Mobility: • Online efficient private traffic • Public transportation on the fly route balancing  Safety: • Detection and support in dangerous situations (e.g. fires, avalanches, etc.)  Health: • Recognition and prediction of epidemic spread  Urban & Local business planning: • Understand economically depressed areas • Help companies to plan investment • A joint project between Copyright alex pentland 2012, all rights reserved Data from individuals Mobile Territorial Lab • Understand the needs and the behaviour of users. • Provide individuals mobile phone equipped with a sensing middleware to collect the data generated to be analyzed (starting community: young families with newborns) • Short term outcomes: 1. Developing and testing a new model of DATA OWNERSHIP 2. Understanding the dynamics of people’s needs 1. Understanding people’s interactions in the generated social networks A joint project between: Copyright alex pentland 2012, all rights reserved Copyright alex pentland 2012, all rights reserved Forbes, 8/10, Mining Human Behavior pentland@mit.edu http://media.mit.edu/~pentland