The Naked Future (21 page)

In 2004 a computer scientist and one of the inventors of RSS, Ramanathan V. Guha, proposed an alternative—but still complementary—theory that status was a bigger factor than balance in who liked what.

Guha, who today works for Google, was the chief architect of Epinions.com, a product review site claiming to offer “real reviews by real people.” In order to distinguish more trustworthy product reviews from less trustworthy ones, Guha created a “Web of trust” system where Epinions users could rate their fellow reviewers as authoritative or not authoritative. As tends to happen in online communities, some users quickly established more clout than others. Guha observed that those individuals who had the highest status had the most pluses attached to them (they were adored) and sent the most number of minuses out.
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Back to our problem. Status theory explains the prettiest-girl-in-the-room syndrome but balance theory much better represents what a functional relationship is like. So which theory works to predict romantic matches? The answer is both.

In 2010 Stanford's Jure Leskovec, who has done work with the Facebook Data Science Team, applied social balance theory and
status theory to Epinions, Wikipedia, and Slashdot users to see which theory predicted how people would form alliances. Epinions and Slashdot allow users to designate “enemies” as well as friends, and Wikipedia allows users to edit the work of others (which can be a signal of an antagonistic relationship).

Using sixteen feature vectors, he found that he could predict friend and foe relations with up to 90 percent accuracy. Now apply this to Facebook, which doesn't allow “unfriendships” or “dislikes” but does allow comments on posts. Those comments that are not accompanied by likes can be correlated with dislikes. It wouldn't be hard to run comments through a semantic machine-learning algorithm to determine key dislike phrases that could more clearly indicate a negative edge (big minus sign). If you've got one friend who is constantly sharing material you aren't fond of—election season tends to bring this stuff out like nothing else can—and you find yourself arguing with her posts, there's a good chance you don't regard your friend as having a terribly high status; you perceive little downside to picking a fight with her. If you notice that one of your friend's friends tends to agree with you, there's a good chance you will wind up being that second friend's friend before too long (friends on Facebook, anyway).
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Facebook, which also serves as a dating site for millions of users, gives a clearer window into status.

If you want a more precise understanding of someone's rank on Facebook, look beyond their friend count to the number of updates they post and the number of likes they get for them. While you may not have much of an interest in predicting which of your friends' friends you will connect with on Facebook, Facebook does have an interest here for reasons we'll get to later in this chapter.

Status and balance scores are what's missing from online dating sites, yet the reason for their absence is obvious. No one would use a dating site that made him feel like a loser with a terrible status score.

But Finkel's research shows that the long-term survivability of a romantic relationship is predictable on the basis of three
variables. Similarity between partners, a category that includes music, religion, educational attainment, income, location, and a host of other things that can (today) be discovered online, is just the
first
one. The other two are how partners collaborate and interact on a day-by-day basis and how partners react to stressful events.
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You are more than a sexual fetish. For that matter, you're more than an income bracket, more than your last educational degree obtained, height-weight proportionality, facial feature symmetry, location, political affiliation, or musical taste. Most pay dating sites try to quantify your personality and some even try to give a number to how you react to different events. But they do this via a survey and that's the problem, because you're more than who you are when you sit down and fill out a form describing who you are. When you reduce yourself to a dating site profile, the result may be closer to the
ultimate you
than the position of the moon when you were born, but perhaps not by that much.

The second variable in predicting relationship longevity that Finkel identifies is collaboration style, a category including communication signals such as how well someone listens, how often or forcefully he interrupts people, and whether he laughs at his own jokes or never at all. Collaboration style includes subtle and nonverbal forms of communication: fidgeting, hand waving, posture, flirtatious glances, and disconcerting stares. These are factors that come into play when people talk about clicking with someone on a first or second date. But collaboration style also comes into play in working relationships and can include such factors as how likely someone is to ask for help when they need it; if she waits until the last possible moment to deliver uncomfortable news; if she seems to whine about every little thing. Whether the answers to these questions are deal breakers for a relationship depends on the unique nature of the couple and the way their communication influences each other. Opposites do sometimes attract because some communication styles have to be complementary, rather than reflective, in order to work. These are the sorts of qualities that simply don't make it into an online dating form, at least not
yet. Measuring how two people communicate and how they collaborate has been historically extremely difficult.

That's beginning to change.

It is the month of April 1998. The setting is the Shibuya neighborhood of Tokyo, the most fashionable four blocks in the trendiest city in the world. Bright lights, two-story advertisements, and enormous television screens look down on the hip teenagers who are gathered below.

Kaori Mikuriya, sixteen, is hanging out with her friends. A signal chimes on her Lovegety, a small, pink, oval-shaped device she recently purchased for 2,900 yen ($25). The chiming indicates that a boy is within the device's fifteen-foot range. No doubt
his
device is going off, too, whoever
he
is. But who is he? She suddenly regrets ever buying this thing. She has no idea what sort of person her device may connect her with. “I started looking around while getting ready to run, if the boy was strange,” Mikuriya later told
Wired
reporter Yukari Iwatani.
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The boy, it turns out, is not “strange.” He's cute. Cute enough. They approach each other and green lights go off on both devices. They're a match, which is to say that they've put their devices on the same setting, of which there are three: “chat,” “let's go enjoy karaoke,” and “get2,” which signals . . . an interest in something beyond karaoke. They adjourn to a
takoyaki
stand for a snack of breaded octopus tentacles.

Takeya Takafuji invented the Lovegety precisely to facilitate these sorts of exchanges because, as he explained to reporters, “Japanese men are very shy.”
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Lovegety couldn't predict a perfect match, but it did boast one important feature that many dating Web sites still can't replicate: it forced people who knew almost nothing about each other to pick an activity and try it together, thereby affording shy Japanese men an opportunity to demonstrate that they could be fun activity partners.
Within four months of launch, Takafuji and his employer, German company Erfolg, had sold 350,000 units around the world.

One of them went to a young, single, British reporter named Charlotte Kemp who wrote of her experience for the
Daily Mirror.
She took the device to her local bar and found, unfortunately, that a matchmaking widget that worked well with shy Japanese boys in Shibuya had a very different effect in a pub on the East End of London. As soon as she turned on her device, the men in the room responded like sharks answering the smell of blood. “Men can be so obvious sometimes,” she observed in her piece about the experience. “They'd all programmed their Lovegetys for the ‘get romantically involved' mode.”
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Eventually, the Lovegety fad fizzled out. Later sales figures revealed why: Kemp's experience was more common than Mikuriya's because a majority of the date-detecting devices were purchased by men.

Skip ahead to May 2004. The setting is CELab, a conference of tech industry executives and luminaries at MIT. Nathan Eagle, who is earning a PhD in wearable computers, is launching an experiment called Serendipity. Each participant at CELab picks up a Nokia 6600, Bluetooth-enabled, Symbian Series 60 phone. The phone is programmed to send icebreaker introductions, which Eagle describes in his thesis as “messages to two proximate individuals who don't know each other but probably should.”
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The icebreakers are a bit like the Lovegety chimes in function, but are more complex in design. Each participant has created an online profile of herself describing what she does, what she's working on, and, in some cases, areas of expertise she's looking for.

A program called BlueDar (developed at the MIT Media Lab by Mat Laibowitz) is running silently in the background of the experiment. Every five minutes it scans the conference area for the locations of each phone so it knows who is standing next to whom. It does this by logging every device's media access control (MAC) address, a hexadecimal number unique to all Internet-enabled devices. Every gadget that collects or sends data over an Ethernet connection has one of these identifiers. While MAC addresses on
desktop-bound machines have been around since 1980, the presence of MAC addresses on phones is, in 2004, an extremely recent phenomenon. Recent and significant.

“That means that my phone can recognize the fact that my laptop is within five meters [sixteen feet]. And the realization that everyone's carrying around these devices that are essentially broadcasting a unique ID means that suddenly you can do social and proximity-based applications,” Eagle told me.

Because each of Eagle's devices has a particular signifier, and all are connected to a larger network, a lot more information can be conveyed about the person to whom each device is connected. Lovegety, remember, provided only three pieces of data: proximity within fifteen feet, gender, and interest in chatting, karaoke, or something more.

Serendipity features in-depth profiles of users and a better matching algorithm. Eagle used a Gaussian mixture model to detect proximity patterns between users and then correlated these patterns with relationship types. The result theoretically should have been better introductions between more like-minded people.

In his write-up of the experiment, Eagle says this was largely the case. A group of VIPs from one very large tech company were delighted to be introduced to a bunch of VIPs from the same company whom they had never before met. But the prettiest-girl-in-the-room syndrome experienced by Charlotte Kemp—with the Lovegety system directing multiple unsuitable matches to her—popped up again in Eagle's experiment. This time the pretty girl was none other than Nicholas Negroponte, who was forced to make a lot of small talk with a Microsoft executive he, in the words of Eagle, “didn't want to talk to.” (Today, Negroponte doesn't remember the experience, but doesn't doubt that it happened precisely as Eagle describes.)

When Eagle deployed the system across the broader MIT campus, he discovered much the same occurrence. Of the one hundred people who participated in the experiment over the course of nine months, women were more cautious and concerned about privacy than were their male counterparts. The one group that most appreciated the experiment was the students from MIT's Sloan School of
Management, arguably the most stodgy and least technological population of the MIT student community. Sloan students were enthusiastic for the opportunity to better network with their peers across other departments. The problem, writes Eagle in his thesis, the other MITers “weren't as excited about getting introduced to Sloan students.”
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Moving the activity of matching from the desktop PC to the real world was supposed to solve the prettiest-girl-in-the-room syndrome. Instead, it spread the problem, contaminating reality.

At the time of Eagle's experiment, large firms were projecting that 80 percent of new mobile phones sold would have Bluetooth capability within two years. “If that prediction holds true, applications like Serendipity would have the potential to transform dramatically the ways in which people meet and connect with each other,” he wrote. “As technologies converge, new mobile phones can identify each other with Bluetooth and can re-create the functionality of the Lovegety by leveraging the information already stored in existing online profiles.”
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The prediction did hold true. The launch of the iPhone and, later, the Google Android system have led to a flood of proximity-based social networking apps, as covered in chapter 2. In terms of matchmaking, all of these share the same fundamental flaw.

Consider this: one of OKCupid's key accomplishments was their mobile app which, after the gay male dating app Grindr, was one of the first dating apps to boast a location-broadcasting feature. Walk into a room, open the app, and—depending on how the other users around have configured their user settings—you get a window into the OKCupid users around you, their likes, preferences, histories, et cetera. One could easily imagine the app becoming the most downloaded program for such situational-awareness devices as the Google Glass headset. Picture yourself putting on a pair of goggles, glancing around a bar packed with singles, and seeing each person in light of how they answered questions about their previous sexual partners. The OKCupid mobile app represents the future of the way we'll interact with proximity-based social networks.