Bordering Fiction

January 24, 2014
Albert-László Barabási

What is absorbing about Dave Eggers's latest novel is not the dystopic world it depicts but the way that arose: Everyone at The Circle, a Google-Amazon-Facebook-Twitter mash-up, is eager to make the world a better place. Engineers at heart, they relentlessly innovate to reduce crime, to organize and store all information, and to leave no one behind. While their motivations are pure, each of their products is a subtle slide toward the 21st century's version of Orwell's 1984—not a world in which a selected few control many but one where everyone monitors everybody. As the company helps us become “all-seeing, all knowing,” privacy becomes a crime, and the upbeat culture of The Circle morphs into an organization whose core values are lifted from the playbook of the U.S. National Security Agency (NSA): “Privacy is theft.” “Secrets are lies.”

A few days into her dream job at The Circle, Mae Holland learns that pleasing consumers forms only a tiny part of her responsibilities. The company demands never-ceasing engagement—an expectation that she fully immerse herself in the wondrous activities the organization offers its employees. She must mingle with politicians, applaud singers, and praise the cooking of celebrity chefs. Most important, she must share all her experiences online. Absenteeism is a sign of detachment. Not sharing is a crime.

Early in the novel, a potential love interest uses Mae's profile to demonstrate an application that packages all online data about her to provide the lowdown for a first date. Mae is outraged as she watches her food allergies and favorite dishes paraded on the big screen. Seemingly, a relationship turned sour before it could really begin. In reality, an episode that defines Mae's trajectory, allowing us to witness her profound transformation over the next 300 pages. Responding to the culture of her new workplace, Mae gives up a little more of her privacy each day. Eventually she becomes the powerful official poster girl of The Circle's open-book philosophy. Her life, with minute resolution, is on display for everyone to witness.

Eggers in The Circle rides a wave that has been brewing for years now. I have argued that given the high predictability of human activity (12), services like the novel's fictitious SeaChange (a vast array of cameras that monitors everyone everywhere) make not only our present but also our future increasingly transparent to the highest bidder (3). Fiction beats nonfiction, however, in its ability to portray the individual motivations—or the lack of them—of the developers that nudge us toward an increasingly transparent society. Eggers's page turner works because it requires no implausible breakthroughs. Its familiarity gets under our skin, as Eggers offers a chilling image not of a distant world but rather of one that feels eerily everyday.

To be sure, some of the plot elements border on incredibility. The United States would never hand over voting and social security to a private company like The Circle. Law-makers would never agree to the lack of privacy The Circle's technologies perpetuate. These are alarmist twists that work only in fiction. But are they? For years, I was told that U.S. laws forbid federal access to my mobile phone records. Then Edward Snowden revealed that NSA did in fact strong-arm that data away from the carriers, jolting me into abandoning my research on anonymized phone records altogether. I also argued that the U.S. government lacks the personnel and know-how to build the sophisticated tools it dreams of using (3). Indeed, some of our best students and colleagues flocked to Facebook, Google, Twitter, and Amazon; I know of no one who chose NSA. Then Snowden revealed that NSA simply purchased the know-how from the Valley. I have thus stopped believing that there is a wall between reality and fairy tales. So I read The Circle not as science fiction but as a case study of a world in which we currently live: a stress test that reboots 1984 for the digital age.

Originally Published in Science 343:6169, 372 (2014)


Figure 1. How hard is to distinguish random from scale-free networks? To show how different are the predictions of the two modeling paradigms, the scale-free and that or the random network models, I show the degree distribution of four systems: Internet at the router level; Protein-protein interaction network of yeast; Email network; Citation network, together with the expected best Poisson distribution fit. It takes no sophisticated statistical tools to notice that the Poisson does not fit.
Box 3: All we need is love

If you have difficulty understanding the need for the super-weak, weakest, weak, strong and strongest classification, you are not alone. It took me several days to get it. So let me explain it in simple terms.

Assume that we want to find the word Love in the following string: "Love". You could of course simply match the string and call it mission accomplished. That, however, would not offer statistical significance for your match.

BC insist that we must use a rigorous algorithm to decide if there is Love in Love. And they propose one, that works like this: Take the original string of letters, and break it into all possible sub-strings: 


They call the match super-strong if at least 90% of these sub-strings matches Love. In this case we do have Love in the list, but it is only one of the 14 possible sub-strings, so Love is not super strong.  

They call the match super-weak if at least 50% of the strings matches the search string. Love is obviously not super-weak either.

At the end Clauset's algorithm arrives to the inevitable conclusion: There is no Love in Love.

The rest of us: Love is all you need

‍Figure 3. Differentiating model systems Curious about the reason the method adopted by BC cannot distinguish the Erdős-Rényi and the scale-free model, we generated the degree distribution of both models for N=5,000 nodes, the same size BC use for their test. We have implemented the scale-free model described in Appendix E of Ref [1], a version of the original scale-free model (their choice is problematic, btw, but let us not dwell on that now). In the plot we  show three different realizations for each network, allowing us to see the fluctuations between different realisations, which are small at this size. The differences between the two models are impossible to miss: The largest nodes in any of the Erdős-Rényi networks have degree less then 20, while the scale-free model generate hubs with hundreds of links. Even a poorly constructed statistical test could tell the difference. Yet,  38% of the time the method used by BC does not identify the scale-free model to be even ‘weak scale-free,’  while 51% of the time it classifies the ER model to be ‘weak scale-free.’


Recent posts
Albert-László Barabási
November 28, 2018

Factors ranging from the timing of a book’s release to its subject matter can determine whether it will crack the vaunted list.

continue reading
Albert-László Barabási
March 6, 2018

A study's failure to find scale-free networks where decades of research has documented their existence offers a cautionary tale on using search criteria that fails elementary tests.

continue reading