Who sits in the 41st chair?

tl;dr Rich-get-richer academic prestige in a scarce job market makes meritocracy impossible. Why some things get popular and others don’t. Also agent-based simulations.

Slightly longer tl;dr This post is about why academia isn’t a meritocracy, at no intentional fault of those in power who try to make it one. None of presented ideas are novel on their own, but I do intend this as a novel conceptual contribution in its connection of disparate threads. Especially, I suggest the predictability of research success in a scarce academic economy as a theoretical framework for exploring successes and failures in the history of science.

But mostly I just beat a “musical chairs” metaphor to death.

Positive Feedback

To the victor go the spoils, and to the spoiled go the victories. Think about it: the Yankees; Alexander the Great; Stanford University. Why do the Yankees have twice as many World Series appearances as their nearest competitors, how was Alex’s empire so fucking vast, and why does Stanford get all the cool grants?

The rich get richer. Enough World Series victories, and the Yankees get the reputation and funding to entice the best players. Ol’ Allie-G inherited an amazing army, was taught by Aristotle, and pretty much every place he conquered increased his military’s numbers. Stanford’s known for amazing tech innovation, so they get the funding, which means they can afford even more innovation, which means even more people think they’re worthy of funding, and so on down the line until Stanford and its neighbors (Google, Apple, etc.) destroy the local real estate market and then accidentally blow up the world.

Alexander's Empire [via]
Alexander’s Empire [via]
Okay, maybe I exaggerated that last bit.

Point is, power begets power. Scientists call this a positive feedback loop: when a thing’s size is exactly what makes it grow larger.

You’ve heard it firsthand when a microphoned singer walks too close to her speaker. First the mic picks up what’s already coming out of the speaker. The mic, doings its job, sends what it hears to an amplifier, sending an even louder version to the very same speaker. The speaker replays a louder version of what it just produced, which is once again received by the microphone, until sound feeds back onto itself enough times to produce the ear-shattering squeal fans of live music have come to dread. This is a positive feedback loop.

Feedback loop. [via]
Feedback loop. [via]
Positive feedback loops are everywhere. They’re why the universe counts logarithmically rather than linearly, or why income inequality is so common in free market economies. Left to their own devices, the rich tend to get richer, since it’s easier to make money when you’ve already got some.

Science and academia are equally susceptible to positive feedback loops. Top scientists, the most well-funded research institutes, and world-famous research all got to where they are, in part, because of something called the Matthew Effect.

Matthew Effect

The Matthew Effect isn’t the reality TV show it sounds like.

For unto every one that hath shall be given, and he shall have abundance: but from him that hath not shall be taken even that which he hath. —Matthew 25:29, King James Bible.

It’s the Biblical idea that the rich get richer, and it’s become a popular party trick among sociologists (yes, sociologists go to parties) describing how society works. In academia, the phrase is brought up alongside evidence that shows previous grant-recipients are more likely to receive new grants than their peers, and the more money a researcher has been awarded, the more they’re likely to get going forward.

The Matthew Effect is also employed metaphorically, when it comes to citations. He who gets some citations will accrue more; she who has the most citations will accrue them exponentially faster. There are many correct explanations, but the simplest one will do here: 

If Susan’s article on the danger of velociraptors is cited by 15 other articles, I am more likely to find it and cite her than another article on velociraptors containing the same information, that has never been citedThat’s because when I’m reading research, I look at who’s being cited. The more Susan is cited, the more likely I’ll eventually come across her article and cite it myself, which in turn increases the likelihood that much more that someone else will find her article through my own citations. Continue ad nauseam.

Some of you are thinking this is stupid. Maybe it’s trivially correct, but missing the bigger picture: quality. What if Susan’s velociraptor research is simply better than the competing research, and that’s why it’s getting cited more?

Yes, that’s also an issue. Noticeably awful research simply won’t get much traction. 1 Let’s disqualify it from the citation game. The point is there is lots of great research out there, waiting to be read and built upon, and its quality isn’t the sole predictor of its eventual citation success.

In fact, quality is a mostly-necessary but completely insufficient indicator of research success. Superstar popularity of research depends much more on the citation effects I mentioned above – more citations begets even more. Previous success is the best predictor of future success, mostly independent of the quality of research being shared.

Example of positive feedback loops pushing some articles to citation stardom.
Example of positive feedback loops pushing some articles to citation stardom. [via]
This is all pretty hand-wavy. How do we know success is more important than quality in predicting success? Uh, basically because of Napster.

Popular Music

If VH1 were to produce a retrospective on the first decade of the 21st century, perhaps its two biggest subjects would be illegal music sharing and VH1’s I Love the 19xx… TV series. Napster came and went, followed by LimeWire, eDonkey2000, AudioGalaxy, and other services sued by Metallica. Well-known early internet memes like Hamster Dance and All Your Base Are Belong To Us spread through the web like socially transmitted diseases, and researchers found this the perfect opportunity to explore how popularity worked. Experimentally.

In 2006, a group of Columbia University social scientists designed a clever experiment to test why some songs became popular and others did not, relying on the public interest in online music sharing. They created a music downloading site which gathered 14,341 users, each one to become a participant in their social experiment.

The cleverness arose out of their experimental design, which allowed them to get past the pesky problem of history only ever happening once. It’s usually hard to learn why something became popular, because you don’t know what aspects of its popularity were simply random chance, and what aspects were genuine quality. If you could, say, just rerun the 1960s, changing a few small aspects here or there, would the Beatles still have been as successful? We can’t know, because the 1960s are pretty much stuck having happened as they did, and there’s not much we can do to change it. 2

But this music-sharing site could rerun history—or at least, it could run a few histories simultaneously. When they signed up, each of the site’s 14,341 users were randomly sorted into different groups, and their group number determined how they were presented music. The musical variety was intentionally obscure, so users wouldn’t have heard the bands before.

A user from the first group, upon logging in, would be shown songs in random order, and were given the option to listen to a song, rate it 1-5, and download it. Users from group #2, instead, were shown the songs ranked in order of their popularity among other members of group #2. Group #3 users were shown a similar rank-order of popular songs, but this time determined by the song’s popularity within group #3. So too for groups #4-#9. Every user could listen to, rate, and download music.

Essentially, the researchers put the participants into 9 different self-contained petri dishes, and waited to see which music would become most popular in each. Ranking and download popularity from group #1 was their control group, in that members judged music based on their quality without having access to social influence. Members of groups #2-#9 could be influenced by what music was popular with their peers within the group. The same songs circulated in each petri dish, and each petri dish presented its own version of history.

Music sharing site from Columbia study.
Music sharing site from Columbia study.

No superstar songs emerged out of the control group. Positive feedback loops weren’t built into the system, since popularity couldn’t beget more popularity if nobody saw what their peers were listening to. The other 8 musical petri dishes told a different story, however. Superstars emerged in each, but each group’s population of popular music was very different. A song’s popularity in each group was slightly related to its quality (as judged by ranking in the control group), but mostly it was social-influence-produced chaos. The authors put it this way:

In general, the “best” songs never do very badly, and the “worst” songs never do extremely well, but almost any other result is possible. —Salganik, Dodds, & Watts, 2006

These results became even more pronounced when the researchers increased the visibility of social popularity in the system. The rich got even richer still. A lot of it has to do with timing. In each group, the first few good songs to become popular are the ones that eventually do the best, simply by an accident of circumstance. The first few popular songs appear at the top of the list, for others to see, so they in-turn become even more popular, and so ad infinitum.  The authors go on:

experts fail to predict success not because they are incompetent judges or misinformed about the preferences of others, but because when individual decisions are subject to social influence, markets do not simply aggregate pre-existing individual preferences.

In short, quality is a necessary but insufficient criteria for ultimate success. Social influence, timing, randomness, and other non-qualitative features of music are what turn a good piece of music into an off-the-charts hit.

Wait what about science?

Compare this to what makes a “well-respected” scientist: it ain’t all citations and social popularity, but they play a huge role. And as I described above, simply out of exposure-fueled-propagation, the more citations someone accrues, the more citations they are likely to accrue, until we get a situation like the Yankees (40 world series appearances, versus 20 appearances by the Giants) on our hands. Superstars are born, who are miles beyond the majority of working researchers in terms of grants, awards, citations, etc. Social scientists call this preferential attachment.

Which is fine, I guess. Who cares if scientific popularity is so skewed as long as good research is happening? Even if we take the Columbia social music experiment at face-value, an exact analog for scientific success, we know that the most successful are always good scientists, and the least successful are always bad ones, so what does it matter if variability within the ranks of the successful is so detached from quality?

Except, as anyone studying their #OccupyWallstreet knows, it ain’t that simple in a scarce economy. When the rich get richer, that money’s gotta come from somewhere. Like everything else (cf. the law of conservation of mass), academia is a (mostly) zero-sum game, and to the victors go the spoils. To the losers? Meh.

So let’s talk scarcity.

The 41st Chair

The same guy who who introduced the concept of the Matthew Effect to scientific grants and citations, Robert K. Merton (…of Columbia University), also brought up “the 41st chair” in the same 1968 article.

Merton’s pretty great, so I’ll let him do the talking:

In science as in other institutional realms, a special problem in the workings of the reward system turns up when individuals or organizations take on the job of gauging and suitably rewarding lofty performance on behalf of a large community. Thus, that ultimate accolade in 20th-century science, the Nobel prize, is often assumed to mark off its recipients from all the other scientists of the time. Yet this assumption is at odds with the well-known fact that a good number of scientists who have not received the prize and will not receive it have contributed as much to the advancement of science as some of the recipients, or more.

This can be described as the phenomenon of “the 41st chair.” The derivation of this tag is clear enough. The French Academy, it will be remembered, decided early that only a cohort of 40 could qualify as members and so emerge as immortals. This limitation of numbers made inevitable, of course, the exclusion through the centuries of many talented individuals who have won their own immortality. The familiar list of occupants of this 41st chair includes Descartes, Pascal, Moliere, Bayle, Rousseau, Saint-Simon, Diderot, Stendahl, Flaubert, Zola, and Proust

[…]

But in greater part, the phenomenon of the 41st chair is an artifact of having a fixed number of places available at the summit of recognition. Moreover, when a particular generation is rich in achievements of a high order, it follows from the rule of fixed numbers that some men whose accomplishments rank as high as those actually given the award will be excluded from the honorific ranks. Indeed, their accomplishments sometimes far outrank those which, in a time of less creativity, proved
enough to qualify men for his high order of recognition.

The Nobel prize retains its luster because errors of the first kind—where scientific work of dubious or inferior worth has been mistakenly honored—are uncommonly few. Yet limitations of the second kind cannot be avoided. The small number of awards means that, particularly in times of great scientific advance, there will be many occupants of the 41st chair (and, since the terms governing the award of the prize do not provide for posthumous recognition, permanent occupants of that chair).

Basically, the French Academy allowed only 40 members (chairs) at a time. We can be reasonably certain those members were pretty great, but we can’t be sure that equally great—or greater—women existed who simply never got the opportunity to participate because none of the 40 members died in time.

These good-enough-to-be-members-but-weren’t were said to occupy the French Academy’s 41st chair, an inevitable outcome of a scarce economy (40 chairs) when the potential number benefactors of this economy far outnumber the goods available (40). The population occupying the 41st chair is huge, and growing, since the same number of chairs have existed since 1634, but the population of France has quadrupled in the intervening four centuries.

Returning to our question of “so what if rich-get-richer doesn’t stick the best people at the top, since at least we can assume the people at the top are all pretty good anyway?”, scarcity of chairs is the so-what.

Since faculty jobs are stagnating compared to adjunct work, yet new PhDs are being granted faster than new jobs become available, we are presented with the much-discussed crisis in higher education. Don’t worry, we’re told, academia is a meritocracy. With so few jobs, only the cream of the crop will get them. The best work will still be done, even in these hard times.

Recent Science PhD growth in the U.S. [via]
Recent Science PhD growth in the U.S. [via]
Unfortunately, as the Columbia social music study (among many other studies) showed, true meritocracies are impossible in complex social systems. Anyone who plays the academic game knows this already, and many are quick to point it out when they see people in much better jobs doing incredibly stupid things. What those who point out the falsity of meritocracy often get wrong, however, is intention: the idea that there is no meritocracy because those in power talk the meritocracy talk, but don’t then walk the walk. I’ll talk a bit later about how, even if everyone is above board in trying to push the best people forward, occupants of the 41st chair will still often wind up being more deserving than those sitting in chairs 1-40. But more on that later.

For now, let’s start building a metaphor that we’ll eventually over-extend well beyond its usefulness. Remember that kids’ game Musical Chairs, where everyone’s dancing around a bunch of chairs while the music is playing, but as soon as the music stops everyone’s got to find a chair and sit down? The catch, of course, is that there are fewer chairs than people, so someone always loses when the music stops.

The academic meritocracy works a bit like this. It is meritocratic, to a point: you can’t even play the game without proving some worth. The price of admission is a Ph.D. (which, granted, is more an endurance test than an intelligence test, but academic success ain’t all smarts, y’know?), a research area at least a few people find interesting and believe you’d be able to do good work in it, etc. It’s a pretty low meritocratic bar, since it described 50,000 people who graduated in the U.S. in 2008 alone, but it’s a bar nonetheless. And it’s your competition in Academic Musical Chairs.

Academic Musical Chairs

Time to invent a game! It’s called Academic Musical Chairs, the game where everything’s made up and the points don’t matter. It’s like Regular Musical Chairs, but more complicated (see Fig. 1). Also the game is fixed.

Figure 1: Academic Musical Chairs
Figure 1: Academic Musical Chairs

See those 40 chairs in the middle green zone? People sitting in them are the winners. Once they’re seated they have what we call in the game “tenure”, and they don’t get up until they die or write something controversial on twitter. Everyone bustling around them, the active players, are vying for seats while they wait for someone to die; they occupy the yellow zone we call “the 41st chair”. Those beyond that, in the red zone, can’t yet (or may never) afford the price of game admission; they don’t have a Ph.D., they already said something controversial on Twitter, etc. The unwashed masses, you know?

As the music plays, everyone in the 41st chair is walking around in a circle waiting for someone to die and the music to stop. When that happens, everyone rushes to the empty seat. A few invariably reach it simultaneously, until one out-muscles the others and sits down. The sitting winner gets tenure. The music starts again, and the line continues to orbit the circle.

If a player spends too long orbiting in the 41st chair, he is forced to resign. If a player runs out of money while orbiting, she is forced to resign. Other factors may force a player to resign, but they will never appear in the rulebook and will always be a surprise.

Now, some players are more talented than others, whether naturally or through intense training. The game calls this “academic merit”, but it translates here to increased speed and strength, which helps some players reach the empty chair when the music stops, even if they’re a bit further away. The strength certainly helps when competing with others who reach the chair at the same time.

A careful look at Figure 1 will reveal one other way players might increase their chances of success when the music stops. The 41st chair has certain internal shells, or rings, which act a bit like that fake model of an atom everyone learned in high-school chemistry. Players, of course, are the electrons.

Electron shells. [via]
Electron shells. [via]
You may remember that the further out the shell, the more electrons can occupy it(-ish): the first shell holds 2 electrons, the second holds 8; third holds 18; fourth holds 32; and so on. The same holds true for Academic Musical Chairs: the coveted interior ring only fits a handful of players; the second ring fits an order of magnitude more; the third ring an order of magnitude more than that, and so on.

Getting closer to the center isn’t easy, and it has very little to do with your “academic rigor”! Also, of course, the closer you are to the center, the easier it is to reach either the chair, or the next level (remember positive feedback loops?). Contrariwise, the further you are from the center, the less chance you have of ever reaching the core.

Many factors affect whether a player can proceed to the next ring while the music plays, and some factors actively count against a player. Old age and being a woman, for example, take away 1 point. Getting published or cited adds points, as does already being friends with someone sitting in a chair (the details of how many points each adds can be found in your rulebook). Obviously the closer you are to the center, the easier you can make friends with people in the green core, which will contribute to your score even further. Once your score is high enough, you proceed to the next-closest shell.

Hooray, someone died! Let’s watch what happens.

The music stops. The people in the innermost ring who have the luckiest timing (thus are closest to the empty chair) scramble for it, and a few even reach it. Some very well-timed players from the 2nd & 3rd shells also reach it, because their “academic merit” has lent them speed and strength to reach past their position. A struggle ensues. Miraculously, a pregnant black woman sits down (this almost never happens), though not without some bodily harm, and the music begins again.

Oh, and new shells keep getting tacked on as more players can afford the cost of admission to the yellow zone, though the green core remains the same size.

Bizarrely, this is far from the first game of this nature. A Spanish boardgame from 1587 called the Courtly Philosophy had players move figures around a board, inching closer to living a luxurious life in the shadow of a rich patron. Random chance ruled their progression—a role of the dice—and occasionally they’d reach a tile that said things like: “Your patron dies, go back 5 squares”.

The courtier's philosophy. [via]
The courtier’s philosophy. [via]
But I digress. Let’s temporarily table the scarcity/41st-chair discussion and get back to the Matthew Effect.

The View From Inside

A friend recently came to me, excited but nervous about how well they were being treated by their department at the expense of their fellow students. “Is this what the Matthew Effect feels like?” they asked. Their question is the reason I’m writing this post, because I spent the next 24 hours scratching my head over “what does the Matthew Effect feel like?”.

I don’t know if anyone’s looked at the psychological effects of the Matthew Effect (if you do, please comment?), but my guess is it encompasses two feelings: 1) impostor syndrome, and 2) hard work finally paying off.

Since almost anyone who reaps the benefits of the Matthew Effect in academia will be an intelligent, hard-working academic, a windfall of accruing success should feel like finally reaping the benefits one deserves. You probably realize that luck played a part, and that many of your harder-working, smarter friends have been equally unlucky, but there’s no doubt in your mind that, at least, your hard work is finally paying off and the academic community is beginning to recognize that fact. No matter how unfair it is that your great colleagues aren’t seeing the same success.

But here’s the thing. You know how in physics, gravity and acceleration feel equivalent? How, if you’re in a windowless box, you wouldn’t be able to tell the difference between being stationary on Earth, or being pulled by a spaceship at 9.8 m/s2 through deep space? Success from merit or from Matthew Effect probably acts similarly, such that it’s impossible to tell one from the other from the inside.

Gravity vs. Acceleration. [via]
Gravity vs. Acceleration. [via]
Incidentally, that’s why the last advice you ever want to take is someone telling you how to succeed from their own experience.

Success

Since we’ve seen explosive success requires but doesn’t rely on skill, quality, or intent, the most successful people are not necessarily in the best position to understand the reason for their own rise. Their strategies may have paid off, but so did timing, social network effects, and positive feedback loops. The question you should be asking is, why didn’t other people with the same strategies also succeed?

Keep this especially in mind if you’re a student, and your tenured-professor advised you to seek an academic career. They may believe that giving you their strategies for success will help you succeed, when really they’re just giving you one of 50,000 admission tickets to Academic Musical Chairs.

Building a Meritocracy

I’m teetering well-past the edge of speculation here, but I assume the communities of entrenched academics encouraging undergraduates into a research career are the same communities assuming a meritocracy is at play, and are doing everything they can in hiring and tenure review to ensure a meritocratic playing field.

But even if gender bias did not exist, even if everyone responsible for decision-making genuinely wanted a meritocracy, even if the game weren’t rigged at many levels, the economy of scarcity (41st chair) combined with the Matthew Effect would ensure a true meritocracy would be impossible. There are only so many jobs, and hiring committees need to choose some selection criteria; those selection criteria will be subject to scarcity and rich-get-richer effects.

I won’t prove that point here, because original research is beyond the scope of this blog post, but I have a good idea of how to do it. In fact, after I finish writing this, I probably will go do just that. Instead, let me present very similar research, and explain how that method can be used to answer this question.

We want an answer to the question of whether positive feedback loops and a scarce economy are sufficient to prevent the possibility of a meritocracy. In 1971, Tom Schelling asked an unrelated question which he answered using a very relevant method: can racial segregation manifest in a community whose every actor is intent on not living a segregated life? Spoiler alert: yes.

He answered this question using by simulating an artificial world—similar in spirit to the Columbia social music experiment, except for using real participants, he experimented on very simple rule-abiding game creatures of his own invention. A bit like having a computer play checkers against itself.

The experiment is simple enough: a bunch of creatures occupy a checker board, and like checker pieces, they’re red or black. Every turn, one creature has the opportunity to move randomly to another empty space on the board, and their decision to move is based on their comfort with their neighbors. Red pieces want red neighbors, and black pieces want black neighbors, and they keep moving randomly ’till they’re all comfortable. Unsurprisingly, segregated creature communities appear in short order.

What if we our checker-creatures were more relaxed in their comforts? They’d be comfortable as long as they were in the majority; say, at least 50% of their neighbors were the same color. Again, let the computer play itself for a while, and within a few cycles the checker board is once again almost completely segregated.

Schelling segregation. [via]
Schelling segregation. [via]
What if the checker pieces are excited about the prospect of a diverse neighborhood? We relax the criteria even more, so red checkers only move if fewer than a third of their neighbors are red (that is, they’re totally comfortable with 66% of their neighbors being black)? If we run the experiment again, we see, again, the checker board breaks up into segregated communities.

Schelling’s claim wasn’t about how the world worked, but about what the simplest conditions were that could still explain racism. In his fictional checkers-world, every piece could be generously interested in living in a diverse neighborhood, and yet the system still eventually resulted in segregation. This offered a powerful support for the theory that racism could operate subtly, even if every actor were well-intended.

Vi Hart and Nicky Case created an interactive visualization/game that teaches Schelling’s segregation model perfectly. Go play it. Then come back. I’ll wait.


Such an experiment can be devised for our 41st-chair/positive-feedback system as well. We can even build a simulation whose rules match the Academic Musical Chairs I described above. All we need to do is show that a system in which both effects operate (a fact empirically proven time and again in academia) produces fundamental challenges for meritocracy. Such a model would be show that simple meritocratic intent is insufficient to produce a meritocracy. Hulk smashing the myth of the meritocracy seems fun; I think I’ll get started soon.

The Social Network

Our world ain’t that simple. For one, as seen in Academic Musical Chairs, your place in the social network influences your chances of success. A heavy-hitting advisor, an old-boys cohort, etc., all improve your starting position when you begin the game.

To put it more operationally, let’s go back to the Columbia social music experiment. Part of a song’s success was due to quality, but the stuff that made stars was much more contingent on chance timing followed by positive feedback loops. Two of the authors from the 2006 study wrote another in 2007, echoing this claim that good timing was more important than individual influence:

models of information cascades, as well as human subjects experiments that have been designed to test the models (Anderson and Holt 1997; Kubler and Weizsacker 2004), are explicitly constructed such that there is nothing special about those individuals, either in terms of their personal characteristics or in their ability to influence others. Thus, whatever influence these individuals exert on the collective outcome is an accidental consequence of their randomly assigned position in the queue.

These articles are part of a large literature in predicting popularity, viral hits, success, and so forth. There’s The Pulse of News in Social Media: Forecasting Popularity by Bandari, Asur, & Huberman, which showed that a top predictor of newspaper shares was the source rather than the content of an article, and that a major chunk of articles that do get shared never really make it to viral status. There’s Can Cascades be Predicted? by Cheng, Adamic, Dow, Kleinberg, and Leskovec (all-star cast if ever I saw one), which shows the remarkable reliance on timing & first impressions in predicting success, and also the reliance on social connectivity. That is, success travels faster through those who are well-connected (shocking, right?), and structural properties of the social network are important. This study by Susarla et al. also shows the importance of location in the social network in helping push those positive feedback loops, effecting the magnitude of success in YouTube Video shares.

Twitter information cascade. [via]
Twitter information cascade. [via]
Now, I know, social media success does not an academic career predict. The point here, instead, is to show that in each of these cases, before sharing occurs and not taking into account social media effects (that is, relying solely on the merit of the thing itself), success is predictable, but stardom is not.

Concluding, Finally

Relating it to Academic Musical Chairs, it’s not too difficult to say whether someone will end up in the 41st chair, but it’s impossible to tell whether they’ll end up in seats 1-40 until you keep an eye on how positive feedback loops are affecting their career.

In the academic world, there’s a fertile prediction market for Nobel Laureates. Social networks and Matthew Effect citation bursts are decent enough predictors, but what anyone who predicts any kind of success will tell you is that it’s much easier to predict the pool of recipients than it is to predict the winners.

Take Economics. How many working economists are there? Tens of thousands, at least. But there’s this Econometric Society which began naming Fellows in 1933, naming 877 Fellows by 2011. And guess what, 60 of 69 Nobel Laureates in Economics before 2011 were Fellows of the society. The other 817 members are or were occupants of the 41st chair.

The point is (again, sorry), academic meritocracy is a myth. Merit is a price of admission to the game, but not a predictor of success in a scarce economy of jobs and resources. Once you pass the basic merit threshold and enter the 41st chair, forces having little to do with intellectual curiosity and rigor guide eventual success (ahem). Small positive biases like gender, well-connected advisors, early citations, lucky timing, etc. feed back into increasingly larger positive biases down the line. And since there are only so many faculty jobs out there, these feedback effects create a naturally imbalanced playing field. Sometimes Einsteins do make it into the middle ring, and sometimes they stay patent clerks. Or adjuncts, I guess. Those who do make it past the 41st chair are poorly-suited to tell you why, because by and large they employed the same strategies as everybody else.

Figure 1: Academic Musical Chairs
Yep, Academic Musical Chairs

And if these six thousand words weren’t enough to convince you, I leave you with this article and this tweet. Have a nice day!

Addendum for Historians

You thought I was done?

As a historian of science, this situation has some interesting repercussions for my research. Perhaps most importantly, it and related concepts from Complex Systems research offer a middle ground framework between environmental/contextual determinism (the world shapes us in fundamentally predictable ways) and individual historical agency (we possess the power to shape the world around us, making the world fundamentally unpredictable).

More concretely, it is historically fruitful to ask not simply what non-“scientific” strategies were employed by famous scientists to get ahead (see Biagioli’s Galileo, Courtier), but also what did or did not set those strategies apart from the masses of people we no longer remember. Galileo, Courtier provides a great example of what we historians can do on a larger scale: it traces Galileo’s machinations to wind up in the good graces of a wealthy patron, and how such a system affected his own research. Using recently-available data on early modern social and scholarly networks, as well as the beginnings of data on people’s activities, interests, practices, and productions, it should be possible to zoom out from Biagioli’s viewpoint and get a fairly sophisticated picture of trajectories and practices of people who weren’t Galileo.

This is all very preliminary, just publicly blogging whims, but I’d be fascinated by what a wide-angle (dare I say, macroscopic?) analysis of the 41st chair in could tell us about how social and “scientific” practices shaped one another in the 16th and 17th centuries. I believe this would bear previously-impossible fruit, since a lone historian grasping ten thousand tertiary actors at once is a fool’s errand, but is a walk in the park for my laptop.

As this really is whim-blogging, I’d love to hear your thoughts.

Notes:

  1. Unless it’s really awful, but let’s avoid that discussion here.
  2. short of a TARDIS.

Bridging Token and Type

There’s an oft-spoken and somewhat strawman tale of how the digital humanities is bridging C.P. Snow’s “Two Culture” divide, between the sciences and the humanities. This story is sometimes true (it’s fun putting together Ocean’s Eleven-esque teams comprising every discipline needed to get the job done) and sometimes false (plenty of people on either side still view the other with skepticism), but as a historian of science, I don’t find the divide all that interesting. As Snow’s title suggests, this divide is first and foremost cultural. There’s another overlapping divide, a bit more epistemological, methodological, and ontological, which I’ll explore here. It’s the nomothetic(type)/idiographic(token) divide, and I’ll argue here that not only are its barriers falling, but also that the distinction itself is becoming less relevant.

Nomothetic (Greek for “establishing general laws”-ish) and Idiographic (Greek for “pertaining to the individual thing”-ish) approaches to knowledge have often split the sciences and the humanities. I’ll offload the hard work onto Wikipedia:

Nomothetic is based on what Kant described as a tendency to generalize, and is typical for the natural sciences. It describes the effort to derive laws that explain objective phenomena in general.

Idiographic is based on what Kant described as a tendency to specify, and is typical for the humanities. It describes the effort to understand the meaning of contingent, unique, and often subjective phenomena.

These words are long and annoying to keep retyping, and so in the longstanding humanistic tradition of using new words for words which already exist, henceforth I shall refer to nomothetic as type and idiographic as token. 1 I use these because a lot of my digital humanities readers will be familiar with their use in text mining. If you counted the number of unique words in a text, you’d be be counting the number of types. If you counted the number of total words in a text, you’d be counting the number of tokens, because each token (word) is an individual instance of a type. You can think of a type as the platonic ideal of the word (notice the word typical?), floating out there in the ether, and every time it’s actually used, it’s one specific token of that general type.

The Token/Type Distinction
The Token/Type Distinction

Usually the natural and social sciences look for general principles or causal laws, of which the phenomena they observe are specific instances. A social scientist might note that every time a student buys a $500 textbook, they actively seek a publisher to punch, but when they purchase $20 textbooks, no such punching occurs. This leads to the discovery of a new law linking student violence with textbook prices. It’s worth noting that these laws can and often are nuanced and carefully crafted, with an awareness that they are neither wholly deterministic nor ironclad.

[via]
[via]
The humanities (or at least history, which I’m more familiar with) are more interested in what happened than in what tends to happen. Without a doubt there are general theories involved, just as in the social sciences there are specific instances, but the intent is most-often to flesh out details and create a particular internally consistent narrative. They look for tokens where the social scientists look for types. Another way to look at it is that the humanist wants to know what makes a thing unique, and the social scientist wants to know what makes a thing comparable.

It’s been noted these are fundamentally different goals. Indeed, how can you in the same research articulate the subjective contingency of an event while simultaneously using it to formulate some general law, applicable in all such cases? Rather than answer that question, it’s worth taking time to survey some recent research.

A recent digital humanities panel at MLA elicited responses by Ted Underwood and Haun Saussy, of which this post is in part itself a response. One of the papers at the panel, by Long and So, explored the extent to which haiku-esque poetry preceded what is commonly considered the beginning of haiku in America by about 20 years. They do this by teaching the computer the form of the haiku, and having it algorithmically explore earlier poetry looking for similarities. Saussy comments on this work:

[…] macroanalysis leads us to reconceive one of our founding distinctions, that between the individual work and the generality to which it belongs, the nation, context, period or movement. We differentiate ourselves from our social-science colleagues in that we are primarily interested in individual cases, not general trends. But given enough data, the individual appears as a correlation among multiple generalities.

One of the significant difficulties faced by digital humanists, and a driving force behind critics like Johanna Drucker, is the fundamental opposition between the traditional humanistic value of stressing subjectivity, uniqueness, and contingency, and the formal computational necessity of filling a database with hard decisions. A database, after all, requires you to make a series of binary choices in well-defined categories: is it or isn’t it an example of haiku? Is the author a man or a woman? Is there an author or isn’t there an author?

Underwood addresses this difficulty in his response:

Though we aspire to subtlety, in practice it’s hard to move from individual instances to groups without constructing something like the sovereign in the frontispiece for Hobbes’ Leviathan – a homogenous collection of instances composing a giant body with clear edges.

But he goes on to suggest that the initial constraint of the digital media may not be as difficult to overcome as it appears. Computers may even offer us a way to move beyond the categories we humanists use, like genre or period.

Aren’t computers all about “binary logic”? If I tell my computer that this poem both is and is not a haiku, won’t it probably start to sputter and emit smoke?

Well, maybe not. And actually I think this is a point that should be obvious but just happens to fall in a cultural blind spot right now. The whole point of quantification is to get beyond binary categories — to grapple with questions of degree that aren’t well-represented as yes-or-no questions. Classification algorithms, for instance, are actually very good at shades of gray; they can express predictions as degrees of probability and assign the same text different degrees of membership in as many overlapping categories as you like.

Here we begin to see how the questions asked of digital humanists (on the one side; computational social scientists are tackling these same problems) are forcing us to reconsider the divide between the general and the specific, as well as the meanings of categories and typologies we have traditionally taken for granted. However, this does not yet cut across the token/type divide: this has gotten us to the macro scale, but it does not address general principles or laws that might govern specific instances. Historical laws are a murky subject, prone to inducing fits of anti-deterministic rage. Complex Systems Science and the lessons we learn from Agent-Based Modeling, I think, offer us a way past that dilemma, but more on that later.

For now, let’s talk about influence. Or diffusion. Or intertextuality. 2 Matthew Jockers has been exploring these concepts, most recently in his book Macroanalysis. The undercurrent of his research (I think I’ve heard him call it his “dangerous idea”) is a thread of almost-determinism. It is the simple idea that an author’s environment influences her writing in profound and easy to measure ways. On its surface it seems fairly innocuous, but it’s tied into a decades-long argument about the role of choice, subjectivity, creativity, contingency, and determinism. One word that people have used to get around the debate is affordances, and it’s as good a word as any to invoke here. What Jockers has found is a set of environmental conditions which afford certain writing styles and subject matters to an author. It’s not that authors are predetermined to write certain things at certain times, but that a series of factors combine to make the conditions ripe for certain writing styles, genres, etc., and not for others. The history of science analog would be the idea that, had Einstein never existed, relativity and quantum physics would still have come about; perhaps not as quickly, and perhaps not from the same person or in the same form, but they were ideas whose time had come. The environment was primed for their eventual existence. 3

An example of shape affording certain actions by constraining possibilities and influencing people. [via]
An example of shape affording certain actions by constraining possibilities and influencing people. [via]
It is here we see the digital humanities battling with the token/type distinction, and finding that distinction less relevant to its self-identification. It is no longer a question of whether one can impose or generalize laws on specific instances, because the axes of interest have changed. More and more, especially under the influence of new macroanalytic methodologies, we find that the specific and the general contextualize and augment each other.

The computational social sciences are converging on a similar shift. Jon Kleinberg likes to compare some old work by Stanley Milgram 4, where he had people draw maps of cities from memory, with digital city reconstruction projects which attempt to bridge the subjective and objective experiences of cities. The result in both cases is an attempt at something new: not quite objective, not quite subjective, and not quite intersubjective. It is a representation of collective individual experiences which in its whole has meaning, but also can be used to contextualize the specific. That these types of observations can often lead to shockingly accurate predictive “laws” isn’t really the point; they’re accidental results of an attempt to understand unique and contingent experiences at a grand scale. 5

Manhattan. Dots represent where people have taken pictures; blue dots are by locals, red by tourists, and yellow unsure. [via Eric Fischer]
Manhattan. Dots represent where people have taken pictures; blue dots are by locals, red by tourists, and yellow are uncertain. [via Eric Fischer]
It is no surprise that the token/type divide is woven into the subjective/objective divide. However, as Daston and Galison have pointed out, objectivity is not an ahistorical category. 6 It has a history, is only positively defined in relation to subjectivity, and neither were particularly useful concepts before the 19th century.

I would argue, as well, that the nomothetic and idiographic divide is one which is outliving its historical usefulness. Work from both the digital humanities and the computational social sciences is converging to a point where the objective and the subjective can peaceably coexist, where contingent experiences can be placed alongside general predictive principles without any cognitive dissonance, under a framework that allows both deterministic and creative elements. It is not that purely nomothetic or purely idiographic research will no longer exist, but that they no longer represent a binary category which can usefully differentiate research agendas. We still have Snow’s primary cultural distinctions, of course, and a bevy of disciplinary differences, but it will be interesting to see where this shift in axes takes us.

Notes:

  1. I am not the first to do this. Aviezer Tucker (2012) has a great chapter in The Oxford Handbook of Philosophy of Social Science, “Sciences of Historical Tokens and Theoretical Types: History and the Social Sciences” which introduces and historicizes the vocabulary nicely.
  2. Underwood’s post raises these points, as well.
  3. This has sometimes been referred to as environmental possibilism.
  4. Milgram, Stanley. 1976. “Pyschological Maps of Paris.” In Environmental Psychology: People and Their Physical Settings, edited by Proshansky, Ittelson, and Rivlin, 104–124. New York.

    ———. 1982. “Cities as Social Representations.” In Social Representations, edited by R. Farr and S. Moscovici, 289–309.

  5. If you’re interested in more thoughts on this subject specifically, I wrote a bit about it in relation to single-authorship in the humanities here
  6. Daston, Lorraine, and Peter Galison. 2007. Objectivity. New York, NY: Zone Books.

Networks Demystified 8: When Networks are Inappropriate

A few hundred years ago, I promised to talk about when not to use networks, or when networks are used improperly. With The Historian’s Macroscope in the works, I’ve decided to finally start answering that question, and this Networks Demystified is my first attempt at doing so. If you’re new here, this is part of an annoyingly long series (1 network basics, 2 degree, 3 power laws, 4 co-citation analysis, 5 communities and PageRank, 6 this space left intentionally blank, 7 co-citation analysis II). I’ve issued a lot of vague words of caution without doing a great job of explaining them, so here is the first substantive part of that explanation.

Networks are great. They allow you to do things like understand the role of postal routes in the circulation of knowledge in early modern Europe, or of the spread of the black death in the middle ages, or the diminishing importance of family ties in later Chinese governments. They’re versatile, useful, and pretty easy in today’s software environment. And they’re sexy, to boot. I mean, have you seen this visualization of curved lines connecting U.S. cities? I don’t even know what it’s supposed to represent, but it sure looks pretty enough to fund!

A really pretty network visualization. [via]
A really pretty network visualization. [via]

So what could possibly dissuade you from using a specific network, or the concept of networks in general? A lot of things, it turns out, and even a big subset of things that belong only to historians. I won’t cover all of them here, but I will mention a few big ones.

An Issue of Memory Loss

Okay, I lied about not knowing what the above network visualization represents. It turns out it’s a network of U.S. air travel pathways; if a plane goes from one city to another, an edge connects the two cities together. Pretty straightforward. And pretty useful, too, if you want to model something like the spread of an epidemic. You can easily see how someone with the newest designer virus flying into Texas might infect half-a-dozen people at the airport, who would in turn travel to other airports, and quickly infect most parts of the country with major airports. Transportation networks like this are often used by the CDC for just such a purpose, to determine what areas might need assistance/quarantine/etc.

The problem is that, although such a network might be useful for epidemiology, it’s not terribly useful for other seemingly intuitive questions. Take migration patterns: you want to know how people travel. I’ll give you another flight map that’s a bit easier to read.

Flight patterns over U.S. [via]
Flight patterns over U.S. [via]
The first thing people tend to do when getting their hands on a new juicy network dataset is to throw it into their favorite software suite (say, Gephi) and run a bunch of analyses on it. Of those, people really like things like PageRank or Betweenness Centrality, which can give the researcher a sense of important nodes in the network based on how central they are; in this case, how many flights have to go through a particular city in order to get where they eventually intend to go.

Let’s look at Las Vegas. By anyone’s estimation it’s pretty important; well-connected to cities both near and far, and pretty central in the southwest. If I want to go from Denver to Los Angeles and a direct flight isn’t possible, Las Vegas seems to be the way to go. If we also had road networks, train networks, cell-phone networks, email networks, and so forth all overlaid on top of this one, looking at how cities interact with each other, we might be able to begin to extrapolate other information like how rumors spread, or where important trade hubs are.

Here’s the problem: network structures are deceitful. They come with a few basic assumptions that are very helpful in certain areas, but extremely dangerous in others, and they are the reason why you shouldn’t analyze a network without thinking through what you’re implying by fitting your data to the standard network model. In this case, the assumption to watch out for is what’s known as a lack of memory.

The basic networks you learn about, with nodes and edges and maybe some attributes, embed no information on how those networks are generally traversed. They have no memories. For the purposes of disease tracking, this is just fine: all epidemiologists generally need to know is whether two people might accidentally happen to find themselves in the same place at the same time, and where they individually go from there. The structure of the network is enough to track the spread of a disease.

For tracking how people move, or how information spreads, or where goods travel, structure alone is rarely enough. It turns out that Las Vegas is basically a sink, not a hub, in the world of airline travel. People who travel there tend to stay for a few days before traveling back home. The fact that it happens to sit between Colorado and California is meaningless, because people tend not to go through Vegas to get from one to another, even though individually, people from both states travel there with some frequency.

If the network had a memory to it, if it somehow knew not just that a lot of flights tended to go between Colorado and Vegas and between LA and Vegas, but also that the people who went to Vegas returned to where they came from, then you’d be able to see that Vegas isn’t the same sort of hub that, say, Atlanta is. Travel involving Vegas tends to be to or from, rather than through. In truth, all cities have their own unique profiles, and some may be extremely central to the network without necessarily being centrally important in questions about that network (like human travel patterns).

The same might be true of letter-writing networks in early modern Europe, my research of choice. We often find people cropping up as extremely central, connecting very important figures whom we did not previously realize were connected, only to find out that later that, well, it’s not exactly what we thought. This new central figure, we’ll call him John Smith, happened to be the cousin of an important statesman, the neighbor of a famous philosopher, and the once-business-partner of some lawyer. None of the three ever communicated with John about any of the others, and though he was structurally central on the network, he was no-one of any historical note. A lack of memory in the network that information didn’t flow through John, only to or from him, means my centrality measurements can often be far from the mark.

It turns out that in letter-writing networks, people have separate spheres: they tend to write about family with family members, their governmental posts with other officials, and their philosophies with other philosophers. The overarching structure we see obscures partitions between communities that seem otherwise closely-knit. When researching with networks, especially going from the visualization to the analysis phase, it’s important to keep in mind what the algorithms you use do, and what assumptions they and your network structure embed in the evidence they provide.

Sometimes, the only network you have might be the wrong network for the job. I have a lot of peers (me included) who try to understand the intellectual landscape of early modern Europe using correspondence networks, but this is a poor proxy indeed for what we are trying to understand. Because of the spurious structural connections, like that of our illustrious John Smith, early modern networks give us a sense of unity that might not have been present at the time.

And because we’re only looking on one axis (letters), we get an inflated sense of the importance of spatial distance in early modern intellectual networks. Best friends never wrote to each other; they lived in the same city and drank in the same pubs; they could just meet on a sunny afternoon if they had anything important to say. Distant letters were important, but our networks obscure the equally important local scholarly communities.

If there’s a moral to the story, it’s that there are many networks that can connect the same group of nodes, and many questions that can be asked of any given network, but before trying to use networks to study history, you should be careful to make sure the questions match the network.

Multimodality

As humanists asking humanistic questions, our networks tend to be more complex than the sort originally explored in network science. We don’t just have people connected to people or websites to websites, we’ve got people connected to institutions to authored works to ideas to whatever else, and we want to know how they all fit together. Cue the multimodal network, or a network that includes several types of nodes (people, books, places, etc.).

I’m going to pick on Elijah Meeks’ map of of the DH2011 conference, because I know he didn’t actually use it to commit the sins I’m going to discuss. His network connected participants in the conference with their institutional affiliations and the submissions they worked on together.

Part of Elijah Meeks' map of DH2011. [via]
Part of Elijah Meeks’ map of DH2011. [via]
From a humanistic perspective, and especially from a Latourian one, these multimodal networks make a lot of sense. There are obviously complex relationships between many varieties of entities, and the promise of networks is to help us understand these relationships. The issue here, however, is that many of the most common metrics you’ll find in tools like Gephi were not created for multimodal networks, and many of the basic assumptions of network research need to be re-aligned in light of this type of use.

Let’s take the local clustering coefficient as an example. It’s a measurement often used to see if a particular node spans several communities, and it’s calculated by seeing how many of a node’s connections are connected to each other. More concretely, if all of my friends were friends with one another, I would have a high local clustering coefficient; if, however, my friends tended not to be friends with one another, and I was the only person in common between them, my local clustering coefficient would be quite low. I’d be the bridge holding the disparate communities together.

If you study the DH2011 network, the problem should become clear: local clustering coefficient is meaningless in multimodal networks. If people are connected to institutions and conference submissions, but not to one another, then everyone must have the same local clustering coefficient: zero. Nobody’s immediate connections are connected to each other, by definition in this type of network.

Local clustering coefficient is an extreme example, but many of the common metrics break down or mean something different when multiple node-types are introduced to the network. People are coming up with ways to handle these networks, but the methods haven’t yet made their way into popular software. Yet another reason that a researcher should have a sense of how the algorithms work and how they might interact with their own data.

No Network Zone

The previous examples pointed out when networks might be used inappropriately, but there are also times when there is no appropriate use for a network. This isn’t so much based on data (most data can become a network if you torture them enough), but on research questions. Networks seem to occupy a similar place in the humanities as power laws do in computational social sciences: they tend to crop up everywhere regardless of whether they actually add anything informative. I’m not in the business of calling out poor uses of networks, but a good rule of thumb on whether you should include a network in your poster or paper is to ask yourself whether its inclusion adds anything that your narrative doesn’t.

Alternatively, it’s also not uncommon to see over-explanations of networks, especially network visualizations. A narrative description isn’t always the best tool for conveying information to an audience; just as you wouldn’t want to see a table of temperatures over time when a simple line chart would do, you don’t want a two-page description of communities in a network when a simple visualization would do.

This post is a bit less concise and purposeful than the others in this series, but stay-tuned for a revamped (and hopefully better) version to show up in The Historian’s Macroscope. In the meantime, as always, comments are welcome and loved and will confer good luck on all those who write them.

Historians, Doctors, and their Absence

[Note: sorry for the lack of polish on the post compared to others. This was hastily written before a day of international travel. Take it with however many grains of salt seem appropriate under the circumstances.]

[Author’s note two: Whoops! Never included the link to the article. Here it is.]

Every once in a while, 1 a group of exceedingly clever mathematicians and physicists decide to do something exceedingly clever on something that has nothing to do with math or physics. This particular research project has to do with the 14th Century Black Death, resulting in such claims as the small-world network effect is a completely modern phenomenon, and “most social exchange among humans before the modern era took place via face-to-face interaction.”

The article itself is really cool. And really clever! I didn’t think of it, and I’m angry at myself for not thinking of it. They look at the empirical evidence of the spread of disease in the late middle ages, and note that the pattern of disease spread looked shockingly different than patterns of disease spread today. Epidemiologists have long known that today’s patterns of disease propagation are dependent on social networks, and so it’s not a huge leap to say that if earlier diseases spread differently, their networks must have been different too.

Don’t get me wrong, that’s really fantastic. I wish more people (read: me) would make observations like this. It’s the sort of observation that allows historians to infer facts about the past with reasonable certainty given tiny amounts of evidence. The problem is, the team had neither any doctors, nor any historians of the late middle ages, and it turned an otherwise great paper into a set of questionable conclusions.

Small world networks have a formal mathematical definition, which (essentially) states that no matter how big the population of the world gets, everyone is within a few degrees of separation from you. Everyone’s an acquaintance of an acquaintance of an acquaintance of an acquaintance. This non-intuitive fact is what drives the insane speeds of modern diseases; today, an epidemic can spread from Australia to every state in the U.S. in a matter of days. Due to this, disease spread maps are weirdly patchy, based more around how people travel than geographic features.

Patchy h5n1 outbreak map.
Patchy h5n1 outbreak map.

The map of the spread of black death in the 14th century looked very different. Instead of these patches, the disease appeared to spread in very deliberate waves, at a rate of about 2km/day.

Spread of the plague, via the original article.
Spread of the plague, via the original article.

How to reconcile these two maps? The solution, according to the network scientists, was to create a model of people interacting and spreading diseases across various distances and types of networks. Using the models, they show that in order to generate these wave patterns of disease spread, the physical contact network cannot be small world. From this, because they make the (uncited) claimed that physical contact networks had to be a subset of social contact networks (entirely ignoring, say, correspondence), the 14th century did not have small world social networks.

There’s a lot to unpack here. First, their model does not take into account the fact that people, y’know, die after they get the plague. Their model assumes infected have enough time and impetus to travel to get the disease as far as they could after becoming contagious. In the discussion, the authors do realize this is a stretch, but suggest that because, people could if they so choose travel 40km/day, and the black death only spread 2km/day, this is not sufficient to explain the waves.

I am no plague historian, nor a doctor, but a brief trip on the google suggests that black death symptoms could manifest in hours, and a swift death comes only days after. It is, I think, unlikely that people would or could be traveling great distances after symptoms began to show.

More important to note, however, are the assumptions the authors make about social ties in the middle ages. They assume a social tie must be a physical one; they assume social ties are connected with mobility; and they assume social ties are constantly maintained. This is a bit before my period of research, but only a hundred years later (still before the period the authors claim could have sustained small world networks), but any early modern historian could tell you that communication was asynchronous and travel was ordered and infrequent.

Surprisingly, I actually believe the authors’ conclusions: that by the strict mathematical definition of small world networks, the “pre-modern” world might not have that feature. I do think distance and asynchronous communication prevented an entirely global 6-degree effect. That said, the assumptions they make about what a social tie is are entirely modern, which means their conclusion is essentially inevitable: historical figures did not maintain modern-style social connections, and thus metrics based on those types of connections should not apply. Taken in the social context of the Europe in the late middle ages, however, I think the authors would find that the salient features of small world networks (short average path length and high clustering) exist in that world as well.

A second problem, and the reason I agree with the authors that there was not a global small world in the late 14th century, is because “global” is not an appropriate axis on which to measure “pre-modern” social networks. Today, we can reasonably say we all belong to a global population; at that point in time, before trade routes from Europe to the New World and because of other geographical and technological barriers, the world should instead have been seen as a set of smaller, overlapping populations. My guess is that, for more reasonable definitions of populations for the time period, small world properties would continue to hold in this time period.

Notes:

  1. Every day? Every two days?

Rippling o’er the Wave

The inimitable Elijah Meeks recently shared his reasoning behind joining Google+ over Twitter or Facebook. “G+ seems to be self-consciously a network graph that happens to let one connect and keep in touch.” For those who haven’t made the jump, Google+ feels like a contact list on steroids; it lets you add contacts, organize them into different (often overlapping) “circles,” and ultimately you can share materials based on those circles, video chat, send messages, and so forth. By linking your pre-existing public Google profile (and rolling in old features like Buzz and Google Reader), Google has essentially socialized web presences rather than “web presencifying” the social space.

It’s a wishy-washy distinction, and not entirely true, but it feels true enough that many who never worried about social networking sites are going to Google+. This is also one of the big distinctions between the loved-but-lost Google Wave, which was ultrasocial but also ultraprivate; it was not an extended Twitter, but an extended AIM or gmail — really some Frankenstein of the two. It wasn’t about presences and extending contacts, but about chatting alone.

True to Google form, they’ve already realized the potential of sharing in this semi-public space. If Twitter weren’t so minimalistic, they too would have caught on early. Yesterday, via G+ itself, Ripples rippled through the social space. Google+ Ripples describes itself as “a way to visualize the impact of any public post.” This link 1 shows the “ripples” of Ripples itself 2, or the propagation of news of Ripples through the G+ space.

They do a great job invoking the very circles used to organize contacts. Nested circles show subsequent generations of the shared post, and in most cases nested circles also represent followers of the most recent root node. Below the graph, G+ displays the posting frequency over time and allows the user to rewind the clock, seeing how the network grew. Hidden at the bottom of the page, you can find the people with the most public reshares (“influencers”), basic network statistics (average path length, not terribly meaningful in this situation; longest chain; and shares-per-hour), and languages of reshared posts. You can also read the reshares themselves on the right side of the screen, which immediately moved this from my mental “toy” box to the “research tool” box.

Make no mistake, this is a research tool. Barring the lack of permanent links or the ability to export the data into some manipulable file 3, this is a perfect example of information propagation done well. When doing similar research on Twitter, one often requires API-programming prowess to get even this far; in G+, it’s as simple as copying a link. By making information-propagating-across-a-network something sexy, interesting, and easily accessible to everyone, Google is making diffusion processes part of the common vernacular. For this, I give Google +1.

 

 

Notes:

  1. One feature I would like would be the ability to freeze Ripples links. The linked content will change as more people share the initial post – this is potentially problematic.
  2. Anything you can do I can do meta.
  3. which will be necessary for this to go from “research tool” to “actually used research tool”