1. Information Targeting and Coordination: An Experimental Study Matthew Hashim Joint work with Karthik Kannan and Sandra Maximiano Purdue University

2. “Everybody Does it” 2 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

3. Widespread Piracy Rates Claimed 3

4. 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) 4

5. Widespread Piracy Rates Claimed Veracity of the estimates are often questioned (GAO Report 2011) 5

6. 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? 6

7. Experiment: Public Good Game Setting that captures – Free-riding behavior – Societal impact due to individual decisions Utility function for consumer i is given by: 7 Individual earningPublic good component

8. 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) 8 Nash equilibrium is not dependent on information targeting

9. Model n consumers; In our experiment n=5 Consumers have identical endowments E i ; E i =50 Consumers simultaneously allocate x i to the public good Combined contribution is subject to thresholds The threshold to offer quality Q is ; presented later

10. Model (cont.) with 10

11. 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 11

12. Experimental Results 12 Contribution to the Group Account for Rounds 2 - 16 Treatmentn Mean ContributionStd. Err. Mean QualityStd. Err. No Information 300 24.580.922.920.04 Random Information 375 24.990.782.960.04 w/ information 168 23.681.162.970.07 w/o information 207 26.051.062.950.06 Target Below 375 32.230.703.690.04 w/ information 172 23.971.033.700.06 w/o information 203 39.230.633.690.05 Target Above 300 36.950.594.270.04 w/ information 163 38.830.794.240.05 w/o information 137 34.720.854.300.06

13. Experimental Results 13 Targeted Treatments

14. Experimental Results 14 Targeted Treatments

15. Experimental Results Random Effects GLS regression: Pooled Data DV: Contribution(1)(2)(3) Random Information0.8672.5302.624 (2.512)(2.263)(2.276) Target Below9.125***5.634*5.326* (2.544)(2.298)(2.318) Target Above12.938***9.352***9.192*** (2.695)(2.434)(2.449) Period Info. (Random)-1.016-1.3501.102 (1.063)(0.959)(1.793) Period Info. (Below)-3.211*-1.6071.522 (1.359)(1.229)(2.281) Period Info. (Above)-1.040-1.8771.539 (1.308)(1.180)(2.425) Beliefs0.544***0.577*** (0.031)(0.037) Beliefs * Period Info.-0.094 (0.058) Constant24.580***8.936***7.988*** (1.838)(1.878)(1.976) Observations1350 R2R2 0.1430.337 Wald X 2 40.99***363.44*** *** p < 0.001, ** p < 0.01, * p < 0.05 15

16. Experimental Results Random Effects GLS regression: Non-Pooled Data DV: ContributionRandom Info.Target BelowTarget Above Period Information-1.422-3.051**2.644* (1.034)(1.038)(1.154) Beliefs0.645***0.422***0.408*** (0.053)(0.061)(0.082) Constant8.873***19.347***20.754*** (2.094)(2.412)(2.913) Observations375 300 R2R2 0.350.3800.115 Wald X 2 151.21***68.05***38.59*** *** p < 0.001, ** p < 0.01, * p < 0.05 16

17. Experimental Results Coordination Waste for Rounds 2 - 16 Mean InefficiencyNo vs. RandRand vs. BelowRand vs. Above No Info27.07 z = 0.11 Random Info26.96 Target Below26.49 z = 0.26 Target Above21.43z = 2.09 17

18. 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 18

19. 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 19

20. 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 20

21. Thank you 21

22. 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 22

23. 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 23

24. 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 24

25. 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 25

26. 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 26

27. 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 27

28. 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 28

29. 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 29