Treasure Hunt: Social Learning in the Field - Google Sites

Treasure Hunt: Social Learning in the Field Markus M. Mobius Harvard University and NBER

Tuan Phan Harvard Business School

Adam Szeidl UC Berkeley and NBER November 2010

Treasure Hunt

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Motivation Introduction

• Motivation • Theory • Double-counting • Echo Chamber

• How is information aggregated in social networks? • Information from friends and relatives may affect important political and economic decisions.

Treasure Hunt Results Conclusion

◦ Voting: Lazarsfeld et al (1944) show US voters more influenced by friends than by mass media in 1940 Presidential election.

◦ Technology adoption: farmers learn from others in Ghana (Conley-Udry, 2010) but not in western Kenya (Duflo et al, 2010).

• New media like Facebook and Twitter may increase role of networks in diffusing and aggregating information.

• This paper: use a field experiment to shed light on mechanism of information aggregation in networks.

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Theory Introduction

• Motivation • Theory • Double-counting • Echo Chamber Treasure Hunt

Two leading theories of social learning in networks:

• DeGroot (1974) model: agents update by averaging opinions of network neighbors.

Results

◦ “Double-count” info reaching them through multiple paths.

Conclusion

◦ DeMarzo, Vayanos and Zwiebel (2003) and Golub and Jackson (2010).

• Rational model: agents follow Bayesian updating. ◦ Tag source of information: “streams” model. ◦ Variants explored in Bala and Goyal (1998), Acemoglu, Bimpikis and Ozdaglarz (2010). Distinguishing models matters, because DeGroot model generates 1. Overconfidence due to treating repeat information as independent; 2. Persistent differences in opinion across clusters. Treasure Hunt

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Tagged vs Untagged Updating Introduction


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Treasure Hunt Results

Receiver

Sender

Conclusion

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• Sender observes a signal and transmits it to receiver through two uninformed intermediaries.

• DeGroot: receiver perceives two signals as independent and hence double-counts them.

• Streams: source of signal is tagged and hence receiver correctly counts it once. Treasure Hunt

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Learning with binary signals Introduction

• Motivation • Theory • Double-counting • Echo Chamber Treasure Hunt Results Conclusion

• An agent learns about binary outcome: can Markus sing? ◦ Prior belief that Markus can sing is μ. • Agent receives conditionally independent binary signals s1 , ..., sn . ◦ Each signal correct with probability q > .5. • If s denotes share of positive signals, posterior belief μ ˆ satisfies log(

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μ q μ ˆ ) = log( ) + n × (2s − 1) × log( ). 1−μ ˆ 1−μ 1−q

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Example 1: Double-counting Introduction


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Receiver

Sender

Conclusion

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Model A: De Groot model

μ q μ ˆ ) = log( ) + 2 × (2s − 1) × log( ) log( 1−μ ˆ 1−μ 1−q

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Receiver

Sender

Conclusion

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Model B: Streams model

μ q μ ˆ ) = log( ) + (2s − 1) × log( ) log( 1−μ ˆ 1−μ 1−q

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I1


Receiver

Sender

Conclusion

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• DeGroot model leads to overconfidence due to treating repeat information as independent (DeMarzo et al, 2003).

• Journalistic rule: If you don’t observe an event yourself have it confirmed by at least three independent sources.

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Example 2: Echo Chamber Introduction


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Sender 2


I1 Receiver

Receiver’s island

Sender 1

I2

• Receiver is more likely to have friendship loops within his own social island (school, workplace, university). Treasure Hunt

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Example 2: Echo Chamber Introduction


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Sender 2


I1 Receiver

Receiver’s island

Sender 1

I2

• De Groot model overweights own-island signals → generates differences in opinion across clusters (Golub and Jackson). Treasure Hunt

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Introduction Treasure Hunt

• Experimental design • Screen shots Results Conclusion

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Field experiment: Overview Introduction Treasure Hunt


1. Elicit social network of about 4,800 students at Harvard (sophomores, juniors and seniors).

• Online elicitation using Facebook with small financial incentives.

• See Leider, Mobius, Rosenblat and Do (2009) for details on “trivia game” technique. 2. Invite subjects to play “Treasure Hunt” online game.

• Game involves collecting information from friends about quiz questions. 3. Track subjects’ conversations and guesses over time.

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The Treasure Hunt Introduction Treasure Hunt

• Subjects received invitation email with link to experiment.

• Experimental design • Screen shots

• They received binary signals on some imaginary treasure.

Results Conclusion

They were told that the majority of subjects had received correct signals.

• They were encouraged to login as often as they liked during a 4-day period and update their best guess.

• Correct guessers received two movie ticket vouchers. • 843 out of 1392 eligible subjects participated (about 25 percent of all juniors and seniors).

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Screen Shots - P1 Introduction

Treasure Hunt

Treasure Hunt


Instructions Welcome to the Treasure Hunt! You will receive two Movie Vouchers to any AMC/Loews movie theater if you find all the correct answers to the three questions below. You have four days until noon of Saturday, May 27. After the game ends, we will send you an email with the correct answers and the winners will have the opportunity to specify a postal address to which the movie vouchers will be sent. These are the three questions:

A treasure was discovered ...

either "at the bottom of the ocean" or "on top of Mount Everest"

The treasure was found by ... either "Julius Caesar" or "Napoleon Bonaparte"

The treasure is buried ...

either "in Larry Summers' office" or "under the John Harvard statue"

On the next two pages we will suggest three answers to you and we will ask you to submit your best guess.

Our suggested answers do not have to be correct. However, for each question the majority of participants in this experiment will receive the correct suggestion. So a good idea would be to talk to other participants of this game (about half of all Juniors and Seniors are invited). While this game is running you can login as many times as you want and modify your guesses.

Next Page >>

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Screen Shots - P2 Treasure Hunt


Suggested Answers

• Experimental design • Screen shots

We have highlighted our suggestions to you in green.

Results Conclusion


at the bottom of the ocean. on top of Mount Everest.

The treasure was found by ... Julius Caesar.

Napoleon Bonaparte.


under the John Harvard statue.

in Larry Summers' office.

About half of all juniors and seniors received invitations. You can view the names of potential participants by simply starting to type their first name, last name or their FAS username in the field below - a list of matches will appear below that field. If no list appears you might be using an old browser and we encourage you to use a more modern browser (such as IE 6, Firefox, Camino or Safari). Search for participants: may

Maya Eden Maya Eden

Maya Frommer

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May Habib

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Screen Shots - P3 Treasure Hunt



You can return to this page as often as you like during the next four days and update your choices if you receive new information or change your mind. If all your choices are correct, you will receive your movie tickets. You submitted your last guess on Tuesday 21st of November 04:50:37 AM which is shown below. Please modify your guess if you changed your mind on a guess. You can use the invitation email to login again as often as you like!


at the bottom of the ocean.

on top of Mount Everest.

The treasure was found by ...

Julius Caesar.

Napoleon Bonaparte.


in Larry Summers' office.

under the John Harvard statue.

Please don't forget to move to the next page so that your new guess gets saved!

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Screen Shots - P5 Introduction

Treasure Hunt

Treasure Hunt

• Experimental design • Screen shots Results

Thank You! We sent you an email to remind you how to login when you want to update your guess.