was no possibility of them spotting a balloon. These two factors played a key role in the success of the MIT approach, as illustrated by the fact that the depth of the tree of invites went up to 15 people, and approximately one of three tweets spreading information about the team originated outside the U.S. Distributing the reward money more broadly motivated a much larger number of people (more than 5,000) to join the team, including some from outside of the U.S. who could be rewarded for simply knowing someone who could find a balloon. This strategy combined the incentive of personal gain with the power of social networks to connect people locating each balloon with the MIT team.

The MIT team received more than 200 submissions of balloon sightings, of which 30 to 40 turned out to be accurate. Given the considerably noisy submission data, including deliberate attempts at misdirection, the team had to develop a strategy to accurately identify the correct sightings. It did not have time to build a sophisticated machine-learning system to automate the process, nor did it have access to a trusted human network to verify balloon sightings. Instead, most of its strategies relied on using human reasoning to analyze the information submitted with the balloon sightings and eliminate submissions with inconsistencies.

The first strategy was to observe the patterns of submissions about a certain balloon site. Since the balloons were all located in public spaces, each one tended to elicit multiple submissions. Multiple submissions at a specific location increased the probability of a report being accurate. However, those deliberately falsifying balloon sightings also submitted multiple sightings for each false location. To filter out these submissions, the team observed differ-ing patterns in how balloon locations were reported (see Figure 3). Multiple submissions about a real balloon location tended to differ a little from one another, reflecting natural variation in representing a certain location: address, crossroads, nearby landmarks. Malicious submissions tended to have identical representations for a single location, making them suspicious.

Another simple strategy the team
used involved comparing the IP ad-
dress of the submission with where
a balloon was reported found; for ex-
ample, one submission reporting a
balloon in Florida came from an IP ad-
dress in the Los Angeles area. A simple
IP trace and common sense filtered
out such false submissions.

Many submissions included pictures, some contrived to confirm misleading submissions. Most altered pictures involved shots of a balloon from a distance and lacked the DARPA agent holding the balloon and the DARPA banner (an unannounced detail). Figure 4 shows examples of authentic and

Figure 3. typical real (top) and false (bottom) locations of balloons, with bottom map depicting five submissions with identical locations.

Figure 4. typical real (left) and contrived (center and right) pictures of balloons.