Social Learning and Consumer Demand

Social Learning and Consumer Demand

Markus Mobius (Harvard University and NBER)Paul Niehaus (Harvard University)Tanya Rosenblat (Wesleyan University and IAS)

28 April, 2006

Introduction

We “seed” a known social network with information by distributing new products randomly to some members.

Methodology: How can we measure the influence of treated agents on their friends?

Introduction

We “seed” a known social network with information by distributing new products randomly to some members.

Methodology: How can we measure the influence of treated agents on their friends?

Results: How does social influence decline with distance?

Methodology

We build a simple model to infer the “interaction probability” between a treated agent and any of her social neighbors.

During an interaction the treated agent’s knowledge is transferred to the neighbor.

Interaction probabilities vary by social distance.

Our model has the advantage that it can be easily estimated and that it can deal with treatment “overlaps”.

Methodology

Interaction probabilities are convenient to measure influence.

Example: Assume that an agent has 10 direct friends and 60 indirect friends and the interaction probabilities are and . Then on average the agent transfers knowledge to 1 direct friends and 3 indirect friends. In this example the agent affects knowledge in the network mainly by influencingindirect friends rather than direct friends because the interaction probability decreases less strongly than the network grows.

1.01 p05.02 p

Basic Design:

Stage 1: Measure Social Network

Stage 2: Baseline Survey

Stage 3: Distribute Products

Stage 4: Track Social Learning

1. Measuring the Social Network

Measuring the Network

Rather than surveys, agents play in a trivia game

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Information [ ed

Account Info:

Name: Paul Niehaus

Member Since: May 18, 2004

Last Update: June 6, 2005

Basic Info: [ ed

School: Harvard '04

Geography: Boston, MA

Status: Grad Student

Sex: Male

Concentration: Economics

Birthday: 03/11/1982

High School: St. John's Prep '00

Contact Info: [ ed

Email:

Screenname: pfn007

Mobile: 508.335.5242

Website: http:/ /www.people.fas.harvard.edu/~nieha

Personal Info: [ ed

Relationship Status: In a Relationship with

Lauren Young (Berkeley)

Interests: visiting / talking to / daydreaming aboutLauren Young

Clubs and Jobs: Americans for Being Awesome

Favorite Music: donkey kong count ry II soundtrack

Favorite Books: The Bible, Development as Freedom, LOTRThe Screwtape Letters , Moneyball, MWG!

Favorite Movies: Kindergarten Cop , Office Space, Friday,Good Will Hunting, Pumping Iron 20thanniversary edition, pretty much any othermovies with Ahnold except Junior, Dr.Strangelove, Kujo's happy bi rthday movie

Favorite Quote: good advice I have received from friends:

"it'll be snowy and cold tomorrow, so kee pwarm and avoid slipperiness."- Yi Qian

"you should have proposed toa heterosexwoman."- Michael Baldwin

"go to grad school. I went, an d I loved it."- Elhanan Helpman

Summer Plans [ ed

Job/Activity: hanging out with Lauren

Location: Cambridge, MA, 02140

Additional Info:also catc

• Markus

• His Profile

• (Ad Space)

• His Friends

Trivia Game: Recruitment

1. On login, each Harvard undergraduate member of thefacebook.com saw an invitation to play in the trivia game.

2. Subjects agree to an informed consent form – now we can email them!

3. Subjects list 10 friends about whom they want to answer trivia questions.

4. This list of 10 people is what we’re interested in (not their performance in the trivia game)

Trivia Game: Trivia Questions

1. Subjects list 10 friends – this creates 10*N possible pairings.

2. Every night, new pairs are randomly selected by the computer

Example: Suppose Markus listed Tanya as one of his 10 friends, and that this pairing gets picked.

Trivia Game Example

a) Tanya (subject) gets an email asking her to log in and answer a question about herself

b) Tanya logs in and answers, “which of the following kinds of music do you prefer?”

Trivia Game Example (cont.)

c) Once Tanya has answered, Markus gets an email inviting him to log in and answer a question about one of his friends.

d) After logging in, Markus has 20 seconds to answer “which of the following kinds of music does Tanya prefer?”

Trivia Game Example (cont.)

e) If Markus’ answer is correct, he and Tanya are entered together into a nightly drawing to win a prize.

Trivia Game: Summary

Subjects have incentives to list the 10 people they are most likely to be able to answer trivia questions about.

This is our (implicit) definition of a “friend”

Answers to trivia questions are unimportant ok if people game the answers as long as the people it’s easiest

to game with are the same as those they know best. Roommates were disallowed 20 second time limit to answer On average subjects got 50% of 4/5 answer multiple choice

questions right – and many were easy

Recruitment

In addition to invitations on login, Posters in all hallways Workers in dining halls with laptops to step through

signup Personalized snail mail to all upper-class students Article in The Crimson on first grand prize winner

Average acquisition cost per subject ~= $2.50

Participation

Consent: 2932 out of 6389 undergrads (46%), and 50% of upperclassmen

10 friends: 2360 undergraduates (37%) Participation by year of graduation:

2005 45%

2006 52%

2007 53%

2008 34%

Participation

By residential house (upperclassmen)

Adams 42% Leverett 50%

Cabot 52% Lowell 48%

Currier 52% Mather 57%

Dunster 60% Pforzheimer 50%

Eliot 48% Quincy 49%

Kirkland 48% Winthrop 43%

Network Data

23,600 links from participants 12,782 links between participants 6,880 of these symmetric (3,440 coordinated friendships)

Similar to 2003 results Construct the network using “or” link definition

5576 out of 6389 undergraduates (87%) participated or were named

One giant cluster Average path length between participants = 4.2 Cluster coefficient for participants = 17%

Lower than 2003 results – because many named friends are in different houses

Growth of Neighborhoods

Average # of roommates: 0.98

Average # of direct friends: 7.68

Average # of SD=2 friends: 57.91

Average # of SD=3 friends: 347.14

Methods in Comparison

2003 House Experiment in 2 undergraduate houses

Email-data: Sacerdote and Marmaris (2004)

Mutual-friend methods with facebook data? (Glaeser, Laibson, Sacerdote 2000)

2. Baseline Survey

Goals of Baseline

We want to predict valuations of subjects for our products without telling them which products we will distribute.

This allows us to test whether subjects with a higher valuations are more influenced.

We treat a product as a vector of attributes which span a space containing the specific product.

Choice of Products

1. We want new products to maximize the potential for social learning.

Choice of Products

1. We want new products to maximize the potential for social learning.

2. We want some products where subjects have to talk to exchange information (such as newspaper subscription) and some products whose use is conspicuous (such as cell phone).

“Public Products”T-Mobile Sidekick II

Philips Key019 Digital Camcorder

Philips ShoqBox

“Private Products”Student Advantage Discount Card (1 year)

Qdoba Meal Vouchers (5)

Baptiste Studios Yoga Vouchers (5)

Configurators

We identified 5 or 6 salient features for each of the six products.

For example, a product might be a general type of discount card for students.

Particular features of the card could be: (i) provides a discount on textbooks; (ii) provides a discount on Amtrak/ Greyhound; etc.

We elicit a baseline valuation from subjects plus a valuation for each feature (assumes additive separability of valuations over features).

Potential additional featuresfor this product include:

Amtrak discounts: studentdiscounts on Amtrak trains.

Textbook discounts: ontextbook purchases atBarnes&Noble.com

Greyhound discounts:student discounts onGreyhound trains.

Online guides: websiteprovides a guide todiscounts by product typeand by city.

Clothing discounts: studentdiscounts at UrbanOutfitters.

14

Baseline bid for StudentDiscount Card

Textbook discounts

6

Clothing discounts

12

Greyhound discounts

0

Amtrak discounts

0

Online guides

0

Feature descriptions

Baseline bid

Feature bids

Constructed Bids We constructed an implicit bid B from subjects responses:

Bid=Baseline Value + Sum over Feature Values (for existing features)

Subjects were told that they could submit a second in the followup survey and that either this bid or the followup bid would be entered with equal probability into a uniform-price auction.

Constructed Bids We constructed an implicit bid B from subjects responses:

Bid=Baseline Value + Sum over Feature Values (for existing features)

Subjects were told that they could submit a second in the followup survey and that either this bid or the followup bid would be entered with equal probability into a uniform-price auction.

Subjects are provided with incentives for truth-telling.

Card Yoga Food Camcorder ShoqBox Sidekick

-50

05

01

00

15

02

00

25

03

00

Distributions of Imputed Bids

$

($20) ($50) ($35) ($150) ($150) ($250)(Price)

Distributions of Imputed Bids

Imputed valuations look sensible.

In each case market prices lie between median bid and upper tail.

3. Distribution of Products

Randomized Product Trials

Private Products1 year Student Advantage cards5 yoga vouchers5 meal vouchers

Public products Try out for approximately 4 weeks during end

of term

Randomization

Only subjects with imputed bids above the median were eligible. We then offered products to about 100 subjects for each product.

Blocked by year of graduation, gender, and residential house.

Email invitations to come pick up samples

Invitation times varied to vary strength of exposure (April 26th – May 3rd)

Response Rates

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Sidekick II Philips ShoqBox Yoga vouchers Student Advantagecard

Key019 camcorder Qdoba vouchers

Overall: 57%

Info Treatments

Varied information communicated verbally by workers doing distribution

Information treatments correspond to product features in our configurators (5 or 6 features for each product).

Reinforced this information treatment with reminder emails

Each treatment given with 50% probability to each subject

Other Treatments

We also provided randomized online and print ads to subjects who did not receive products (not reported in this talk).

4. Track Social Learning

Followup Survey

We measure both subjective and objective knowledge of all subjects.

Followup Survey


Subjective Knowledge:

Stated probability that subject can answer any Yes/No question correctly.

Followup Survey


Subjective Knowledge:

Stated probability that subject can answer any Yes/No question correctly.

Objective Knowledge:

Average number of actual correct Yes/No questions in subsequent quiz.

Eliciting Confidence Levels

Meet “Bob the Robot” and his clones Bob 1 – Bob 100

Subjects are randomly paired with an (unknown) Bob

Subjects indicated a “cutoff Bob” at which they are indifferent about who should answer the question

If assigned Bob is better than the cutoff, Bob answers the question; otherwise we use subject’s answer

Incentive-compatible mechanism to elicit subject’s belief that he/she will get the question right

Facebook Experiment

Second Product

T-Mobile Sidekick IITime left: 36

How confident are you that you can answer some YES/NOquestions about this product correctly?Your confidence: ______ percent

You can increase your earnings by 50 cents if your answer to the followingquestion is not more than 10 percent off.

Please estimate the average confidence of other participants in thisstudy to answer some YES/NO question about this product correctly?______ percent

Next Page >>

Facebook Experiment

Second Product

T-Mobile Sidekick IITime left: 84

Question 1Does the Sidekick include AOL messenger?

YES NO

Your confidence:______ percent

Question 2Does the Sidekick have a color screen?

YES NO


Question 3Does the Sidekick have 10 or more hours of batterylife?

YES NO


Question 4Does the Sidekick have a QWERTY keyboard?

YES NO


Question 5Does the Sidekick include a camera?

YES NO


Question 6Does the Sidekick use the Pocket PC OS?

YES NO


Final Valuations

Also asked for a second bid for each product.

Asked subjects about the valuations of other randomly selected subjects.

Facebook Experiment

First Product

Personal Sound Systemwith MP3 players

Time left: 46

This product is a Personal Sound System,an MP3 player with two inbuilt speakers loudenough to fill a room. It is small enough to fitin your pocket and you can upload songsdirectly from your computer.

Please submit your bid for this product:______ Dollars

You can increase your earnings by 50 cents if youranswer to the following question is not more than10 percent off.

What is your best guess for the averagebid of all other participants?: ______ Dollars

Facebook Experiment

First Product

Personal Sound System with MP3 playersFor each of the following students please predict how they will bid in the auction. For each student if your answer is within10 percent of their true bid we will add10 cents to your earnings.

Danielle Sassoon (FR, Canaday) ______ Dollars Skyler Johnson (FR, Canaday) ______ Dollars

Rachel Thornton (FR, Canaday) ______ Dollars Danny Mou (FR, Canaday) ______ Dollars

Analysis

Model

Model

An untreated (uninformed) subject has a probability p of interacting with some treated (informed) subject.

The interaction probability p depends on the social distance between uninformed and informed subject.

We distinguish three types of social distances: room mates (M), direct friends (NW1) and indirect friends (NW2).

Model

We define knowledge as the subjective or objective probability of answering a question about the product correctly.

If an informed and uninformed subject interact the knowledge of the informed subject is transferred to the uninformed subject (informed = treated with a product).

Model

We define knowledge as the subjective or objective probability of answering a question about the product correctly.

If an informed and uninformed subject interact the knowledge of the informed subject is transferred to the uninformed subject (informed = treated with a product).

After interacting the uninformed subject has the same probability of answering a question correctly as the informed subject.

Model Assume that the knowledge of an informed subject is and the

knowledge of an uninformed subject is .

Assume that the uninformed’s probability of interacting with some informed subject is X. Then we can express the final expected knowledge of the uninformed agent as:

UniformedInformedFinal FXFXF )1(

InfFUnifF

What is X?Assume that the uninformed agent has room mates who were

offered a product, direct friends and indirect friends. Then we can express X as:

21 )1()1()1(1 21NWNWR n

NWn

NWn

R pppX

Rn1NWn 2NWn

What is X?Assume that the uninformed agent has room mates who were

offered a product, direct friends and indirect friends. Then we can express X as:

21 )1()1()1(1 21NWNWR n

NWn

NWn

R pppX

Rn1NWn 2NWn

The probability of interacting with some informed subject is 1 minus theprobability of interacting with none of them.

Model We obtain:

21 )1()1()1)(( 21NWNWR n

NWn

NWn

RUniformedInformedFinalInformed pppFFFF

• We observe and in the followup survey.InfF FinalF

Model We obtain:

21 )1()1()1)(( 21NWNWR n

NWn

NWn


• We observe and in the followup survey.

• We do not observe because we cannot do a baseline quiz without revealing the product.

InfF FinalF

UniformedF

Model We obtain expression (*):

21 )1()1()1)(( 21NWNWR n

NWn

NWn


• We observe and in the followup survey.

• However, we do not observe because we cannot do a baseline quiz without revealing the product.

• Moreover, we expect the information of uninformed agents to vary with the number of eligible neighbors (and hence the number of neighbors who were offered a treatment) due to selection.

InfF FinalF

UniformedF

We instead compare agents in similar “cells”:

NW2 friends: Eligible, Treated


Roommate (M) friends: Eligible , Treated

Subject without product

We instead compare untreated agents in similar “cells”:





We say the green subject lives in a (1,4+,4) cell to indicate that she has onetreated room-mate, and four treated NW1 and NW2 friends AND she has at least one more eligible (but non-treated) NW1 friend (indicated by plus sign).

For example, compare a (1,4+,4) cell with a (1,5,4) cell:









For example, compare a (1,4+,4) cell with a (1,5,4) cell:









The green agent on the right faces the same neighborhood as the agent on the leftbut the randomization turned one eligible, untreated agent into a treated agent.

Model

By dividing expression (*) for all agents in cell (1,5,4) by expression (*) for all agents in cell (1,4+,4) we obtain the marginal impact of treating one more NW1 neighbor:

1)4,4,1()4,4,1(

)4,5,1()4,5,1(

1 NWFinalInformed

FinalInformed pFF

FF

Model


1)4,4,1()4,4,1(

)4,5,1()4,5,1(

1 NWFinalInformed

FinalInformed pFF

FF

Since we only have finitely many observations per cell we get an estimate forp. For each marginal comparison between two neighboring cells we get a newestimate. From this we can construct an estimate for p and a confidence interval.

Model


1)4,4,1()4,4,1(

)4,5,1()4,5,1(

1 NWFinalInformed

FinalInformed pFF

FF

By comparing neighboring cells we are essentially differing out the unobserved knowledge of the uninformed agent.

Analysis

Results

Results

We are estimating the interaction probabilities separately for each product.

We use both subjective knowledge (“What is the probability that you can answer a Yes/No question correctly?”) and objective knowledge (“Actual share of correctly answered questions in the quiz”).

Results - Card

0.45

0.51

0.09

0.14 0.13

0.01

0.1

.2.3

.4.5

Inte

raction P

robability

M NW1 NW2

card

Objective Knowledge Subjective Knowledge

Results - Card

0.45

0.51

0.09

0.14 0.13

0.01

0.1

.2.3

.4.5

Inte

raction P

robability

M NW1 NW2

card


SE (0.16)* (0.21)* (0.02)* (0.04)* (0.09) (0.03)

Results - Yoga

0.49

0.61

0.11

0.20

0.01

0.12

0.2

.4.6

Inte

raction P

robabili

ty

M NW1 NW2

yoga


SE (0.19)* (0.23)* (0.04)* (0.03)* (0.03) (0.05)*

Results – Restaurant

0.30

0.24

0.120.10

0.01 0.01

0.1

.2.3

Inte

raction P

robability

M NW1 NW2

food


SE (0.03)* (0.08)* (0.03)* (0.04)* (0.02) (0.01)

Results – Camcorder

0.62

0.67

0.12 0.13

0.04 0.05

0.2

.4.6

.8In

tera

ction P

robability

M NW1 NW2

camcorder


SE (0.02)* (0.02)* (0.02)* (0.03)* (0.02)* (0.02)*

Results – MP3

0.58

0.52

0.120.08

0.04 0.04

0.2

.4.6

Inte

raction P

robability

M NW1 NW2

sound


SE (0.06)* (0.07)* (0.03)* (0.04)* (0.02)* (0.01)*

Results – PDA

0.36

0.45

0.120.16

0.06 0.05

0.1

.2.3

.4.5

Inte

raction P

robability

M NW1 NW2

pda


SE (0.04)* (0.07)* (0.03)* (0.04)* (0.02) (0.02)

Results

For “private products” the interaction probability for NW2 neighbors is usually insignificant.

For “public products” the NW2 effect is small but significant.

NW2 neighborhoods are also 7-times as large as NW1 neighborhoods! Therefore, the expected number of influenced NW2 agents can be large.

Results

We would expect that agents with higher implied bids (from baseline survey) should have

greater incentive to learn about the product

higher probability of being talked to by treated guys (assuming that treated agents know the interests of their friends)

Results

We would expect that agents with higher implied bids (from baseline survey) should have

greater incentive to learn about the product

higher probability of being talked to by treated guys (assuming that treated agents know the interests of their friends)

We therefore repeat the previous analysis and only compare high-implicit-bid agents across cells.

Results - Card

0.51

0.70

0.140.17

0.01 0.02

0.2

.4.6

.8In

tera

ction P

robability

M NW1 NW2

card

Subjective Knowledge Subjective Knowledge (HV)

Results - Yoga

0.610.65

0.200.18

0.120.15

0.2

.4.6

Inte

raction P

robability

M NW1 NW2

yoga


Results – Restaurant

0.240.28

0.10

0.15

0.010.03

0.1

.2.3

Inte

raction P

robability

M NW1 NW2

food


Results – Camcorder

0.67

0.80

0.130.17

0.050.07

0.2

.4.6

.8In

tera

ction P

robability

M NW1 NW2

camcorder


Results – MP3

0.520.55

0.08

0.16

0.040.01

0.2

.4.6

Inte

raction P

robability

M NW1 NW2

sound


Results – PDA

0.45

0.55

0.160.18

0.05 0.06

0.2

.4.6

Inte

raction P

robability

M NW1 NW2

pda


Results

Generally, the interaction probability is greater towards high-value subjects.

Results

Generally, the interaction probability is greater towards high-value subjects.

This is consistent with the idea that high-value agents either pay more attention to social learning or are talked to more often by product owners who know their interests.

Who is influenced the most by social learning (close or distant neighbors)?(expected number of interactions taking Nhood size into account; subjective knowledge and significant probabilities only)

M NW1 NW2 TOTAL

CARD 0.50 1.12 1.62

YOGA 0.60 1.60 1.20

FOOD 0.24 0.80 1.04

CAM. 0.65 1.12 2.85 4.62

SOUND 0.50 0.64 2.28 3.42

PDA 0.45 1.44 2.85 4.74

Who is influenced the most by social learning (close or distant neighbors)?(expected number of interactions taking Nhood size into account; subjective knowledge and significant probabilities only)

M NW1 NW2 TOTAL

CARD 0.50 1.12 1.62

YOGA 0.60 1.60 1.20

FOOD 0.24 0.80 1.04

CAM. 0.65 1.12 2.85 4.62

SOUND 0.50 0.64 2.28 3.42

PDA 0.45 1.44 2.85 4.74

Although there is a greater probability to interact with close agents the expected number of interactions increases with distance.

Summary Novel design

Hedonic analysis using configurators Measure of confidence using the Bobs

Simple model of social learning provides interpretable “interaction probabilities”.

Documents

Social Learning and Consumer Demand