Information Targeting and Coordination: An Experimental Study

Matthew Hashim

Joint work with Karthik Kannan and Sandra MaximianoPurdue University

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“Everybody Does it”Housing bubble Chris Engle, in prison for taking a liar loan: “Everybody was doing it because it was simply the way it was done” – NY Times, March 25, 2011

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Widespread Piracy Rates Claimed

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Digital Piracy

• Organizations claim enormous loss due to piracy– $50+ billion lost in business software piracy (BSA 2009)– Global music piracy causes $12.5 billion of economic losses

every year (IPI 2011)– Movie piracy results in $20.5 billion of economic loss (IPI

2007)

– FBI and Commerce officials rely on industry statistics (GAO Report)

– Piracy dominates international trade discussions (e.g., China)

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Widespread Piracy Rates Claimed

• Veracity of the estimates are often questioned (GAO Report 2011)

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Research Questions

• Seemingly different combating strategies– Teen drinking: attack the notion of “everybody does

it”– Piracy: Organizations don’t seem to be doing so

• Does the manner in which piracy information is provided further the “everybody does it” attitude and also increase piracy?– Information targeted equally?– Does high-piracy embolden some to become pirates?

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Experiment: Public Good Game

• Setting that captures– Free-riding behavior– Societal impact due to individual decisions

• Utility function for consumer i is given by:

Individual earning Public good component

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Experimental Treatments

• Information: Rate of free-riding in the game

– No Information feedback

– Random Information feedback

– Target Below feedback (consumers who contributed below the average last round)

– Target Above feedback (consumers who contributed above the average last round)

Nash equilibrium is not dependent on information targeting

Model

• n consumers; In our experiment n=5• Consumers have identical endowments Ei; Ei=50

• Consumers simultaneously allocate xi to the public good

• Combined contribution is subject to thresholds

• The threshold to offer quality Q is ; presented later

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Model (cont.)

• • with

Quality

Threshold as a % of total endowment

Poor 0 0 0%

Medium 50 18.5 20% Good 100 45.5 40%

Very Good 150 81 60% Excellent 200 125 80%

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Procedures and implementation

• Subjects recruited 20-25 per session• Sessions conducted at the Vernon Smith

Experimental Economics Laboratory (VSEEL)• Instructions read aloud• Utilized control questions• Randomly chose 3 periods for payment• Average payout was $12.60 for approx. 1 hour• Subjects interfaced with a z-Tree implementation

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Experimental Results

Contribution to the Group Account for Rounds 2 - 16

Treatment nMean

Contribution Std. Err.Mean

Quality Std. Err.No Information 300 24.58 0.92 2.92 0.04Random Information 375 24.99 0.78 2.96 0.04

w/ information 168 23.68 1.16 2.97 0.07w/o information 207 26.05 1.06 2.95 0.06

Target Below 375 32.23 0.70 3.69 0.04w/ information 172 23.97 1.03 3.70 0.06

w/o information 203 39.23 0.63 3.69 0.05Target Above 300 36.95 0.59 4.27 0.04

w/ information 163 38.83 0.79 4.24 0.05w/o information 137 34.72 0.85 4.30 0.06

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Experimental Results

2 3 4 5 6 7 8 9 10 11 12 13 14 15 160

5

10

15

20

25

30

35

40

45

No InfoRandom InfoTarget BelowTarget Above

Round

Mea

n Co

ntrib

ution

(in

toke

ns)

Targeted Treatments

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2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

No InfoRandom InfoTarget BelowTarget Above

Round

Mea

n Q

ualit

y Le

vel

Poor

Medium

Good

Very Good

Excellent

Experimental ResultsTargeted Treatments

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Experimental ResultsRandom Effects GLS regression: Pooled Data

DV: Contribution (1) (2) (3)Random Information 0.867 2.530 2.624

(2.512) (2.263) (2.276)Target Below 9.125*** 5.634* 5.326*

(2.544) (2.298) (2.318)Target Above 12.938*** 9.352*** 9.192***

(2.695) (2.434) (2.449)Period Info. (Random) -1.016 -1.350 1.102

(1.063) (0.959) (1.793)Period Info. (Below) -3.211* -1.607 1.522

(1.359) (1.229) (2.281)Period Info. (Above) -1.040 -1.877 1.539

(1.308) (1.180) (2.425)Beliefs 0.544*** 0.577***

(0.031) (0.037)Beliefs * Period Info. -0.094

(0.058)Constant 24.580*** 8.936*** 7.988***

(1.838) (1.878) (1.976)Observations 1350 1350 1350R2 0.143 0.337 0.337Wald X2 40.99*** 363.44*** 363.44****** p < 0.001, ** p < 0.01, * p < 0.05

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Experimental Results

Random Effects GLS regression: Non-Pooled DataDV: Contribution Random Info. Target Below Target AbovePeriod Information -1.422 -3.051** 2.644*

(1.034) (1.038) (1.154)Beliefs 0.645*** 0.422*** 0.408***

(0.053) (0.061) (0.082)Constant 8.873*** 19.347*** 20.754***

(2.094) (2.412) (2.913)Observations 375 375 300R2 0.35 0.380 0.115Wald X2 151.21*** 68.05*** 38.59****** p < 0.001, ** p < 0.01, * p < 0.05

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Experimental Results

Coordination Waste for Rounds 2 - 16

Mean Inefficiency No vs. Rand Rand vs. Below Rand vs. Above

No Info 27.07 z = 0.11

Random Info 26.96

Target Below 26.49 z = 0.26

Target Above 21.43 z = 2.09

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Experimental Results

• We believe that inequity aversion is not the only mechanism affecting coordination– Conditional cooperators (Fischbacher, et al. 2001)

• Conditional cooperation as a mechanism motivating coordination among subjects

Information Treatment

Unconditional Cooperators

Conditional Cooperators

Unconditionally Selfish

Random 28.0% 48.0% 24.0% Target Below 21.7% 17.4% 60.9% Target Above 45.0% 50.0% 5.0%

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Conclusion

• Our problem is motivated from a real-world scenario

• Our goal is to explore information targeting strategies and their influence on coordination– Randomly providing information to subjects is similar to

not providing information at all– Targeted information improves coordination

• Targeting above reinforces the behavior of those contributing more than the average

• Targeting below is initially helpful, but eventually results in a degradation of coordination

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Conclusion

• We noticed the role of unconditional and conditional cooperators impacting the information targeting strategies

• Note that random information approximates the approaches currently being used

• Our findings may be useful in developing mitigating strategies for piracy

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• Thank you

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Experimental Findings

• No Information vs. Random Information– No difference in coordination or quality attained

• Random Information vs. Targeted Information– Targeted information allows subjects to coordinate at

higher levels– Targeted information leads to relatively stable

coordination among subjects– Targeting information to those subjects contributing

above the mean performs the best

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Why Experiment?

• Collecting data about dishonest actions is in general difficult

• Such naturally-occurring data may not allow us to study policy implications

• In an experimental lab, the problem can be studied using a controlled setting

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Digital Music/Games/Movies: Public Good?

• A debate exists– Most agree the goods are non-

rivaling: consumption by one consumer does not prevent consumption by other

– Not much agreement on non-excludability: whether copyright laws can protect exclusionary usage

• RIAA and music organizations would prefer it to be excludable

• Economists:– Varian (1998): Information goods are like public goods – Cox (2010): Using piracy data from Finland

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Our Focus

• Digital content (such as Music/Games/Movies) as a public good– Economists have provided reasoning for that– Some firms already treat their digital products as a public

good and have adopted the gift-exchange idea for payments• Radiohead Music and World of Goo: Pay-your-own-price

• Impact of piracy on quality of innovation has been a key issue (Oberholzer-Gee and Strumpf, 2007 and 2010)– We also study how targeting of information affects piracy

and, as a consequence, the quality of the provision– We model our context as a multi-threshold public good game

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Behavioral Predictions

• Bolton and Ockenfels (2000) model of inequity aversion– Inequity based upon comparisons to the group

average rather than the individual– Appropriate model for our game based upon our

approach to delivering information to subjects

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Behavioral Predictions

• Our predictions– Targeted feedback will result in a different level of

coordination than the random feedback treatment• We expect random feedback to coordinate at symmetric

contribution levels• We expected targeted feedback to coordinate at

asymmetric contribution levels

– Targeted below results in a more efficient equilibrium• Inequity aversion should push the contributions in one

direction or the other, dependent on the treatment

– No feedback will face difficulty with coordination

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Experimental Design• 5 players per group

• Random re-matching of players each period– To avoid Reputation and reciprocity effects– Group assignment randomly determined by the computer each period– Subjects were never informed who is in their group

• Elicit subjects’ expectations about the contribution of the group at the start of each period– No Incentives provided for beliefs

• Subjects make contribution decision simultaneously

• Each subject learns their quality level attained and profit earned each period

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Information feedback and Demographic Questions

• For the new group, the decision from the previous round is used for targeting:– An average allocation is calculated for each new group based

upon the subjects that are in the new group– The average allocation is then presented to those subjects

that are to receive information– The same algorithm is used to calculate the number of

subjects that receive random information – providing a comparable stock between information treatments

• Demographic questions were also asked toward the end