Post on 09-Mar-2018
transcript
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 C
onte
nt
Pro
cess
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
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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
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7
Using sensors in
smart phones to
obtain different type
of social networks.
Call Network
Proximity Network
Friendship Network
Co-location Network
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45% accuracy predicting
app downloads. Gompertz
function describes influence
Social Exposure Predicts Behavior
Pan, Aharony, Pentland
65 young families, 12 months data
8
process idp
rocess id
(c)
1 2 3 4 5 6
123456
Inverse Problem: Discovery of Influence, Node State
Raw Observations From Nodes clean state estimate for nodes
influence structure connecting nodes
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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
Pickard, Pan, Rahwan,Cebrian,Madan,Crane,Pentland Copyright alex pentland 2012
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
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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
upsa
uu
LocalaueuΝxEP 1|1Prob
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 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Trend Penetration
Pro
ba
bili
ty
Probability of Idea-Behavior Flow, Φ(C)
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Social trading: users can see and copy trades of another user
Φ(C) and the
Wisdom of the Crowd
Altshuler, Pentland Copyright 2012 Alex Pentland All Rights Reserved
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
Cu
mm
ula
tiv
e d
aily
RO
I
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
Cu
mm
ula
tiv
e d
aily
RO
I
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
Ret
urn
on
Inve
stm
ent
Soci
al v
s n
on
-so
cial
inve
sto
rs
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 50 100 150 200 250 300 350 400 450 500-50%
0
50%
100%
150%
200%
250%
Trading days
RO
I
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
RO
I in
%
(So
cia
l – N
on
-So
cia
l)
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’
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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
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BAC Call Center Productivity Study
Average Call Handle Time
Enga
gem
ent:
f Fa
ce-t
o-F
ace
Net
wo
rk
Productivity correlated
with group engagement
Phase 1
Phase 2
Optimize Idea Flow Φ(C)
Energy
En
ga
ge
me
nt
Average Call Handling Time
Olguin, Waber, Kim, Pentland Copyright alex pentland 2012 all rights reserved
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