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Yinan Zhu Karim AtiyehDarpa Network ChallengeThe ChallengeTo locate ten moored red weather balloons located at ten fixed locationsvisible from nearby roadways and accompanied by DARPA representativesDisplayed for 6/9 hours on the East Coast/West CoastSubmitted coordinates must be within 1 mile

The Basic RulesA submission consists of 10 balloon coordinatesA maximum of 25 submissions per entrantNo penalty for wrong submissionsCan share prizeThe MIT Teams ApproachA recursive mechanism to incentivize both balloon finding and information propagation. Payoff: If Alice (k = 1) invites Bob (k = 2), who then invites Carol (k = 3), who finds the balloon, then they receive payoff

So Alice gets $500, Bob gets $1,000, and Carol gets $2,000.

The Interface

Empirical Results845 trees in 3 days stemming from MIT root node. Largest tree: 602 nodes. Deepest tree: 14 levels. Attrition rate of 56% (close to half the nodes lead to new ones)Finished in less than 9 hours

Incentive Compatibility: reportingNot Sybil ProofAfter sighting a balloon, an individual can gain arbitrarily close to $4,000 by creating a chain of false identities, and reporting from the last.Alternatively, after being invited, create a chain of arbitrary length, and invite from the end of the chain. This doubles the expected payoff. Possible Solutions:Recurring costs and fees (infeasible in this setting)Resource Testing (implied in this setting)In practice, no one did this. The paper attributes this to not enough time. Dishonest ReportingFrom the MIT Balloon Site:

Q: How do you rule out the dishonest reports of spotting the balloons?

A: This is one of the most interesting parts to the challenge! We will use sophisticated algorithms from the field of network science and complex systems theories along with machine learning algorithms to identify valid submissions.

What?Incorrect Reporting

All balloons locations we receivedAfter filtering using ip address geo-coding

True balloon locationsSlide Courtesy of Wei PanSolutionsIP Address comparison to report locationPhoto AnalysisDeployment of trusted members. Reverse White Pages / phone calls

Cut and PasteDARPA Balloon ImageMalicious SubmissionIncentive Compatibility, PropagationTwo fundamental assumptions: 1. each individual in the world, regardless of whether or not she is in the network, has probability p of finding a balloon. In this case, inviting others is clearly incentive compatible. 2. each individual in the network of size n has probability 1/n of finding a balloon. In this case, there are competing interests: On the plus side, you get the trickled payoff of your children. On the other side, you dilute the chances of finding a balloon yourself.Under fairly mild conditions, assumption two also leads to incentive compatibility. Ignoring network effects, these two scenarios mark the best and worst case scenarios propagation. So anything in between will also be incentive compatible. Proof of Propagation IncentivesUnder the second (harsher assumption)Proof: recruit all is a Nash Equilibrium.

Proof (cont.)The proof of agents never opting for partial recruitment proceeds similarly. The generalization of the proof from trees to general graphs follows intuitively. In a general graph, you choosing not to recruit your neighbors may induce others to recruit those same neighbors in your stead.But if it is optimal for you to recruit all your neighbors where the alternative is them being left out of the network, it is definitely optimal when there is a possibility that they will enter the network anyway. Finally, notice that there is no incentive to start a root node after receiving an invitation, because an individuals payoff does not depend on her ancestors. Elements studied by DARPAMedia OutletsOnline BlogsInternet Traffic and search trendsAlerted several scientists (MIT, CMU, Duke, Stanford) interested in the study of social experimentsPre Challenge : Monitoring Diffusion29 Oct : DNC announcment1 Nov : Slashdot posts announcment2 Nov : DNC Wiki for teams4 Nov / 13 Nov: Youtube videos 24 Nov : Registration begins1 Dec : NYTimes, MSNBC4 Dec : CNN Tech Page5 Dec : Balloons launched and MITML interviewKey FindingsViral progression only realized during the first week before launch.After NY Times article : 1000 to 20000 nv/day.Some team efforts went viral during the final days

Traditional Media is more predictable and reliable for a specific message. Viral diffusion if it occurs is very rapid 5000 to 90000 followers for Nerdfighters team.Post Challenge : Performance FactorsMedia coverageExisting vs New social networkWeb crawling methods (Twitter)Search engine rankingMobilization and dispatch abilityFalse report rejection strategyTeam overall strategyTeam network hierarchyGeo-location tools usedMarketing to bring new membersRecursive incentivized recruitingExtraction from internet sourcesAutomated means of extractionAutomated reporting capabilityDispatching membersWebsite design (secure, appealing)Search rank optimization (.edu links )Network hierarchies

Some numbers4367 individuals registered922 submissions50 serious teams350 000 people participated1 Million people exposedMIT team reached about 5400 people and found all balloons in under 9 hours.Notable teamsGTRI George Hotz GroundspeakMIT RBRNerdfighters (vlog)DeciNenaArmy of Eyes iPhone app

GTRI (Broadcast network / charitable goal)George Hotz (Existing network 4 Twitter, 4 Trades)Groundspeak (Existing network, geo mobilization)MIT RBR (similar to MITML media coverage)Nerdfighters : 2k seeking + 3k misinforming + trusted phonebook addressesDeciNena (posts on DNC blogs)

22Observations and AnalysisSocial networks emerged / mobilized within less than a dayFast dispatching (< 2 hours)Mobilization was not a significant factorMass Media > Viral media (ongoing analysis)Importance of Media exposureTwitter is a great Data source (requires filtering)Facebook referralsNature of the task (importance of moral goal)

Further thoughts and discussionGeo-location experiment in foreign country, led by foreign leader.2 teams: One committing an act , the other finding.Targeted message mobilization experiment.

DISCUSSIONNo financial reward.Exponential decay payoffs (vs fixed)Time factor vs mechanism robustness