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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| DARPA Network Challenge | 2/3 | https://en.wikipedia.org/wiki/DARPA_Network_Challenge | reference | science, encyclopedia | 2026-05-05T12:44:48.672218+00:00 | kb-cron |
We're giving $2000 per balloon to the first person to send us the correct coordinates, but that's not all -- we're also giving $1000 to the person who invited them. Then we're giving $500 whoever invited the inviter, and $250 to whoever invited them, and so on ... (see how it works). It might play out like this. Alice joins the team, and we give her an invite link like http://balloon.media.mit.edu/alice. Alice then e-mails her link to Bob, who uses it to join the team as well. We make a http://balloon.media.mit.edu/bob link for Bob, who posts it to Facebook. His friend Carol sees it, signs up, then twitters about http://balloon.media.mit.edu/carol. Dave uses Carol's link to join ... then spots one of the DARPA balloons! Dave is the first person to report the balloon's location to us, and the MIT Red Balloon Challenge Team is the first to find all 10. Once that happens, we send Dave $2000 for finding the balloon. Carol gets $1000 for inviting Dave, Bob gets $500 for inviting Carol, and Alice gets $250 for inviting Bob. The remaining $250 is donated to charity. The strategy was a variant of the Query Incentive Network model of Kleinberg and Raghavan, with the main difference being that the incentive rewards in the team's technique scale down for later participants. The recursive nature of the reward had two beneficial effects. First, participants had an incentive to involve others, as these new people would not become competitors for the reward but rather cooperating partners. Second, people not located in the United States were motivated to participate by passing along information even though they had no way of locating a balloon in person. This helped the team garner a large number (over 5,000) of participants. The team only began with four initial participants. To determine whether submissions were legitimate or fake, the team employed at least three strategies. The first strategy was examining whether there were multiple submissions for a location. If this was the case, then the likelihood of a balloon actually being there was thought to be higher. A second strategy was to check whether the IP address of the submitter matched the supposed location of the balloon. A third strategy was to examine photos accompanying the submission. Real photos included a DARPA employee and a DARPA banner, details which were not announced, while faked ones did not. A detailed analysis of the winning strategy highlighted the important role that social media played. Analysis of Twitter data showed that while some teams relied on large initial bursts of activity over Twitter, mentions of those teams quickly faded. It was argued that due to the recursive incentive structure, the MIT team was able to create a more sustained social media impact than most teams.
== Second-place strategy == The second-place GTRI team used a strategy that relied heavily on Internet publicity and social media. They created a Web site three weeks before the launch day and used a variety of media-related efforts, including a Facebook group, in order to increase the visibility of the team and increase the chance that people who spotted the balloons would report the sightings to them. The team promised to donate all winnings to charity to appeal to the altruism of participants. However, due to the lack of a structure that created much incentive as the winning MIT team's scheme, their network of participants grew to only about 1,400 people. With regard to validating submissions, the team assumed that because of the charitable nature of their effort, the number of false submissions would be low. In any case, they primarily relied on personal validation, having phone conversations with submitters.
== Tenth-place strategy ==
The tenth-place iSchools team, which represented five universities, tried two distinct approaches. The first was directly recruiting team members to look for the balloons on launch day. These members included students, faculty, and alumni on official mailing lists and social media website groups for organizations on the team (e.g., Pennsylvania State University). Only a few of these observers actually participated, however, and only one balloon was found using this strategy. The second strategy was using open-source intelligence methods to do cyberspace searching for results related to the challenge. This was the main source of their success in locating balloons. This strategy, in turn, consisted of two distinct sub-strategies. The first was to use a group of human analysts who would manually search online on a variety of information sources, including Twitter and the websites of competing teams, compile reported sightings, and then evaluate the validity of sightings based on the reputation of the sources. Another strategy relating to cyberspace searching that the team used was an automated Web crawler which captured data from Twitter and opposing teams' websites and then analyzed it. This technology worked slowly and would have benefited from a longer contest duration, but the Twitter crawler proved to be especially useful because tweets sometimes contained geographic information. To confirm the validity of possible sightings, recruited team members were used when possible. If none were available, new observers were recruited from organizations located near the sighting. The distributed location of the different organizations in the team allowed this to be a feasible strategy. Photographic analysis was used to confirm or dispute the validity of claims. The team also encountered a case of another team accidentally leaking information about a sighting and then trying to cover it up. The iSchools team used a variety of information sources, including social networks, to determine what the real location was. This demonstrated the possibility of using information from a wide variety of public websites to determine the validity of something.