Social Physics and the Data Driven...

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

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


7

Using sensors in

smart phones to

obtain different type

of social networks.

Call Network

Proximity Network

Friendship Network

Co-location Network


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


Understanding Ourselves:

Behavioral Demographics


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


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


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



•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



•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)


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


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


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

)


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 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


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 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


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


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


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:



Forbes, 8/10, Mining Human Behavior

[email protected]

http://media.mit.edu/~pentland

mailto:[email protected]

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Social Physics and the Data Driven...

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