Below is some crazy, uninformed ramblings about the least-complex possible way to trick someone into thinking a computer is a human, for the purpose of history research. I’d love some genuine AI/Machine Intelligence researchers to point me to the actual discussions on the subject. These aren’t original thoughts; they spring from countless sci-fi novels and AI research from the ’70s-’90s. Humanists beware: this is super sci-fi speculative, but maybe an interesting thought experiment.
If someone’s chatting with a computer, but doesn’t realize her conversation partner isn’t human, that computer passes the Turing Test. Unrelatedly, if a robot or piece of art is just close enough to reality to be creepy, but not close enough to be convincingly real, it lies in the Uncanny Valley. I argue there is a useful concept in the simplest possible computer which is still convincingly human, and that computer will be at the Turing Point. 1
Forgive my twisting Turing Tests and Uncanny Valleys away from their normal use, for the sake of outlining the Turing Point concept:
A human simulacrum is a simulation of a human, or some aspect of a human, in some medium, which is designed to be as-close-as-possible to that which is being modeled, within the scope of that medium.
A Turing Test winner is any human simulacrum which humans consistently mistake for the real thing.
An occupant of the Uncanny Valley is any human simulacrum which humans consistently doubt as representing a “real” human.
Between the Uncanny Valley and Turing Test winners lies the Turing Point, occupied by the least-sophisticated human simulacrum that can still consistently pass as human in a given medium. The Turing Point is a hyperplane in a hypercube, such that there are many points of entry for the simulacrum to “phase-transition” from uncanny to convincing.
Extending the Turing Test
The classic Turing Test scenario is a text-only chatbot which must, in free conversation, be convincing enough for a human to think it is speaking with another human. A piece of software named Eugene Goostman sort-of passed this test in 2014, convincing a third of judges it was a 13-year-old Ukrainian boy.
There are many possible modes in which a computer can act convincingly human. It is easier to make a convincing simulacrum of a 13-year-old non-native English speaker who is confined to text messages than to make a convincing college professor, for example. Thus the former has a lower Turing Point than the latter.
Playing with the constraints of the medium will also affect the Turing Point threshold. The Turing Point for a flesh-covered robot is incredibly difficult to surpass, since so many little details (movement, design, voice quality, etc.) may place it into the Uncanny Valley. A piece of software posing as a Twitter user, however, would have a significantly easier time convincing fellow users it is human.
The Turing Point, then, is flexible to the medium in which the simulacrum intends to deceive, and the sort of human it simulates.
From Type to Token
Convincing the world a simulacrum is any old human is different than convincing the world it is some specific human. This is the token/type distinction; convincingly simulating a specific person (token) is much more difficult than convincingly simulating any old person (type).
Simulations of specific people are all over the place, even if they don’t intend to deceive. Several Twitter-bots exist as simulacra of Donald Trump, reading his tweets and creating new ones in a similar style. Perhaps imitating Poe’s Law, certain people’s styles, or certain types of media (e.g. Twitter), may provide such a low Turing Point that it is genuinely difficult to distinguish humans from machines.
Put differently, the way some Turing Tests may be designed, humans could easily lose.
It’ll be useful to make up and define two terms here. I imagine the concepts already exist, but couldn’t find them, so please comment if they do so I can use less stupid words:
A type-bot is a machine designed to be represent something at the type-level. For example, a bot that can be mistaken for some random human, but not some specific human.
A token-bot is a machine designed to represent something at the token-level. For example, a bot that can be mistaken for Donald Trump.
This all got me thinking, if we reach the Turing Point for some social media personalities (that is, it is difficult to distinguish between their social media presence, and a simulacrum of it), what’s to say we can’t reach it for an entire social media ecosystem? Can we take a snapshot of Twitter and project it several seconds/minutes/hours/days into the future, a bit like a meteorological model?
A few questions and obvious problems:
Much of Twitter’s dynamics are dependent upon exogenous forces: memes from other media, real world events, etc. Thus, no projection of Twitter alone would ever look like the real thing. One can, however, potentially use such a simulation to predict how certain types of events might affect the system.
This is way overkill, and impossibly computationally complex at this scale. You can simulate the dynamics of Twitter without simulating every individual user, because people on average act pretty systematically. That said, for the humanities-inclined, we may gain more insight from the ground-level of the system (individual agents) than macroscopic properties.
This is key. Would a set of plausibly-duplicate Twitter personalities on aggregate create a dynamic system that matches Twitter as an aggregate system? That is, just because the algorithms pass the Turing Test, because humans believe them to be humans, does that necessarily imply the algorithms have enough fidelity to accurately recreate the dynamics of a large scale social network? Or will small unnoticeable differences between the simulacrum and the original accrue atop each other, such that in aggregate they no longer act like a real social network?
The last point is I think a theoretically and methodologically fertile one for people working in DH, AI, and Cognitive Science: whether reducing human-appreciable traits between machines and people is sufficient to simulate aggregate social behavior, or whether human-appreciability (i.e., Turing Test) is a strict enough criteria for making accurate predictions about societies.
These points aside, if we ever do manage to simulate specific people (even in a very limited scope) as token-bots based on the traces they leave, it opens up interesting pedagogical and research opportunities for historians. Scott Enderle tweeted a great metaphor for this:
@scott_bot In one of the thousand science fiction stories I’ll never write, “nachlass” is the name for an archived consciousness.
Imagine, as a student, being able to have a plausible discussion with Marie Curie, or sitting in an Enlightenment-era salon. 2 Or imagine, as a researcher (if individual Turing Point machines do aggregate well), being able to do well-grounded counterfactual history that works at the token level rather than at the type level.
Turing Point Simulations
Bringing this slightly back into the realm of the sane, the interesting thing here is the interplay between appreciability (a person’s ability to appreciate enough difference to notice something wrong with a simulacrum) and fidelity.
We can specifically design simulation conditions with incredibly low-threshold Turing Points, even for token-bots. That is to say, we can create a condition where the interactions are simple enough to make a bot that acts indistinguishably from the specific human it is simulating.
At the most extreme end, this is obviously pointless. If our system is one in which a person can only answer “yes” or “no” to pre-selected preference questions (“Do you like ice-cream?”), making a bot to simulate that person convincingly would be trivial.
Putting that aside (lest we get into questions of the Turing Point of a set of Turing Points), we can potentially design reasonably simplistic test scenarios that would allow for an easy-to-reach Turing Point while still being historiographically or sociologically useful. It’s sort of a minimization problem in topological optimizations. Such a goal would limit the burden of the simulation while maximizing the potential research benefit (but only if, as mentioned before, the difference between true fidelity and the ability to win a token-bot Turing Test is small enough to allow for generalization).
In short, the concept of a Turing Point can help us conceptualize and build token-simulacra that are useful for research or teaching. It helps us ask the question: what’s the least-complex-but-still-useful token-simulacra? It’s also kind-of maybe sort-of like Kolmogorov complexity for human appreciability of other humans: that is, the simplest possible representation of a human that is convincing to other humans.
I’ll end by saying, once again, I realize how insane this sounds, and how far-off. And also how much an interloper I am to this space, having never so much as designed a bot. Still, as Bill Hart-Davidson wrote,
@scott_bot I agree, and that scale is more clearly plausible than it all seemed years ago reading Mona Lisa Overdrive
the possibility seems more plausible than ever, even if not soon-to-come. I’m not even sure why I posted this on the Irregular, but it seemed like it’d be relevant enough to some regular readers’ interests to be worth spilling some ink.
The name itself is maybe too on-the-nose, being a pun for turning point and thus connected to the rhetoric of singularity, but ¯\_(ツ)_/¯ ↩
Yes yes I know, this is SecondLife all over again, but hopefully much more useful. ↩
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.
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.
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.
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.
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 cited. That’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.
This is all pretty hand-wavy. How do we know success is more important than quality in predicting success? Uh, basically because of Napster.
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.
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.
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.
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.
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 Philosophyhad 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”.
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.
Incidentally, that’s why the last advice you ever want to take is someone telling you how to succeed from their own experience.
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.
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.
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.
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.
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.
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.
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!
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.
Unless it’s really awful, but let’s avoid that discussion here. ↩
History and astronomy are a lot alike. When people claim history couldn’t possibly be scientific, because how can you do science without direct experimentation, astronomy should be used as an immediate counterexample.
Astronomers and historians both view their subjects from great distances; too far to send instruments for direct measurement and experimentation. Things have changed a bit in the last century for astronomy, of course, with the advent of machines sensitive enough to create earth-based astronomical experiments. We’ve also built ships to take us to the farthest reaches, for more direct observations.
It’s unlikely we’ll invent a time machine any time soon, though, so historians are still stuck looking at the past in the same way we looked at the stars for so many thousands of years: through a glass, darkly. Like astronomers, we face countless observational distortions, twisting the evidence that appears before us until we’re left with an echo of a shadow of the past. We recreate the past through narratives, combining what we know of human nature with the evidence we’ve gathered, eventually (hopefully) painting ever-clearer pictures of a time we could never touch with our fingers.
Some take our lack of direct access as a good excuse to shake away all trappings of “scientific” methods. This seems ill-advised. Retaining what we’ve learned over the past 50 years about how we construct the world we see is important, but it’s not the whole story, and it’s got enough parallels with 17th century astronomy that we might learn some lessons from that example.
In the summer 1610, Galileo observed Saturn through a telescope for the first time. He wrote with surprise that
the star of Saturn is not a single star, but is a composite of three, which almost touch each other, never change or move relative to each other, and are arranged in a row along the zodiac, the middle one being three times larger than the two lateral ones…
This curious observation would take half a century to resolve into what we today see as Saturn’s rings. Galileo wrote that others, using inferior telescopes, would report seeing Saturn as oblong, rather than as three distinct spheres. Low and behold, within months, several observers reported an oblong Saturn.
What shocked Galileo even more, however, was an observation two years later when the two smaller bodies disappeared entirely. They appeared consistently, with every observation, and then one day poof they’re gone. And when they eventually did come back, they looked remarkably odd.
Saturn sometimes looked as though it had “handles”, one connected to either side, but the nature of those handles were unknown to Galileo, as was the reason why sometimes it looked like Saturn had handles, sometimes moons, and sometimes nothing at all.
Saturn was just really damn weird. Take a look at these observations from Gassendi a few decades later:
What the heck was going on? Many unsatisfying theories were put forward, but there was no real consensus.
Enter Christiaan Huygens, who in the 1650s was fascinated by the Saturn problem. He believed a better telescope was needed to figure out what was going on, and eventually got some help from his brother to build one.
The idea was successful. Within short order, Huygens developed the hypothesis that Saturn was encircled by a ring. This explanation, along with the various angles we would be viewing Saturn and its ring from Earth, accounted for the multitude of appearances Saturn could take. The figure below explains this:
The explanation, of course, was not universally accepted. An opposing explanation by an anti-Copernican Jesuit contested that Saturn had six moons, the configuration of which accounted for the many odd appearances of the planet. Huygens countered that the only way such a hypothesis could be sustained would be with inferior telescopes.
While the exact details of the dispute are irrelevant, the proposed solution was very clever, and speaks to contemporary methods in digital history. The Accademia del Cimento devised an experiment that would, in a way, test the opposing hypotheses. They built two physical models of Saturn, one with a ring, and one with six satellites configured just-so.
In 1660, the experimenters at the academy put the model of a ringed Saturn at the end of a 75-meter / 250-foot hallway. Four torches illuminated the model but were obscured from observers, so they wouldn’t be blinded by the torchlight. Then they had observers view the model through various quality telescopes from the other end of the hallway. The observers were essentially taken from the street, so they wouldn’t have preconceived notions of what they were looking at.
Depending on the distance and quality of the telescope, observers reported seeing an oblong shape, three small spheres, and other observations that were consistent with what astronomers had seen. When seen through a glass, darkly, a ringed Saturn does indeed form the most unusual shapes.
In short, the Accademia del Cimento devised an experiment, not to test the physical world, but to test whether an underlying reality could appear completely different through the various distortions that come along with how we observe it. If Saturn had rings, would it look to us as though it had two small satellites? Yes.
This did not prove Huygens’ theory, but it did prove it to be a viable candidate given the observational instruments at the time. Within a short time, the ring theory became generally accepted.
The Battle of Trafalgar
So what’s Saturn’s ring have to do with the price of tea in China? What about digital history?
The importance is in the experiment and the model. You do not need direct access to phenomena, whether they be historical or astronomical, to build models, conduct experiments, or generally apply scientific-style methods to test, elaborate, or explore a theory.
In October 1805, Lord Nelson led the British navy to a staggering victory against the French and Spanish during the Napoleonic Wars. The win is attributed to Nelson’s unusual and clever battle tactics of dividing his forces in columns perpendicular to the single line of the enemy ships. Twenty-seven British ships defeated thirty-three Franco-Spanish ones. Nelson didn’t lose a single British ship lost, while the Franco-Spanish fleet lost twenty-two.
But let’s say the prevailing account is wrong. Let’s say, instead, due to the direction of the wind and the superior weaponry of the British navy, victory was inevitable: no brilliant naval tactician required.
This isn’t a question of counterfactual history, it’s simply a question of competing theories. But how can we support this new theory without venturing into counterfactual thinking, speculation? Obviously Nelson did lead the fleet, and obviously he did use novel tactics, and obviously a resounding victory ensued. These are indisputable historical facts.
It turns out we can use a similar trick to what the Accademia del Cimento devised in 1660: pretend as though things are different (Saturn has a ring; Nelson’s tactics did not win the battle), and see whether our observations would remain the same (Saturn looks like it is flanked by two smaller moons; the British still defeated the French and Spanish).
It turns out, further, that someone’s already done this. In 2003, two Italian physicists built a simulation of the Battle of Trafalgar, taking into account details of the ships, various strategies, wind direction, speed, and so forth. The simulation is a bit like a video game that runs itself: every ship has its own agency, with the ability to make decisions based on its environment, to attack and defend, and so forth. It’s from a class of simulations called agent-based models.
When the authors directed the British ships to follow Lord Nelson’s strategy, of two columns, the fleet performed as expected: little loss of life on behalf of the British, major victory, and so forth. But when they ran the model without Nelson’s strategy, a combination of wind direction and superior British firepower still secured a British victory, even though the fleet was outnumbered.
…[it’s said] the English victory in Trafalgar is substantially due to the particular strategy adopted by Nelson, because a different plan would have led the outnumbered British fleet to lose for certain. On the contrary, our counterfactual simulations showed that English victory always occur unless the environmental variables (wind speed and direction) and the global strategies of the opposed factions are radically changed, which lead us to consider the British fleet victory substantially ineluctable.
Essentially, they tested assumptions of an alternative hypothesis, and found those assumptions would also lead to the observed results. A military historian might (and should) quibble with the details of their simplifying assumptions, but that’s all part of the process of improving our knowledge of the world. Experts disagree, replace simplistic assumptions with more informed ones, and then improve the model to see if the results still hold.
The Parable of the Polygons
This agent-based approach to testing theories about how society works is exemplified by the Schelling segregation model. This week the model shot to popularity through Vi Hart and Nicky Case’s Parable of the Polygons, a fabulous, interactive discussion of some potential causes of segregation. Go click on it, play through it, experience it. It’s worth it. I’ll wait.
Finished? Great! The model shows that, even if people only move homes if less than 1/3rd of their neighbors are the same color that they are, massive segregation will still occur. That doesn’t seem like too absurd a notion: everyone being happy with 2/3rds of their neighbors as another color, and 1/3rd as their own, should lead to happy, well-integrated communities, right?
Wrong, apparently. It turns out that people wanting 33% of their neighbors to be the same color as they are is sufficient to cause segregated communities. Take a look at the community created in Parable of the Polygons under those conditions:
This shows that very light assumptions of racism can still easily lead to divided communities. It’s not making claims about racism, or about society: what it’s doing is showing that this particular model, where people want a third of their neighbors to be like them, is sufficient to produce what we see in society today. Much like Saturn having rings is sufficient to produce the observation of two small adjacent satellites.
More careful work is needed, then, to decide whether the model is an accurate representation of what’s going on, but establishing that base, that the model is a plausible description of reality, is essential before moving forward.
Digital history is a ripe field for this sort of research. Like astronomers, we cannot (yet?) directly access what came before us, but we can still devise experiments to help support our research, in finding plausible narratives and explanations of the past. The NEH Office of Digital Humanities has already started funding workshops and projects along these lines, although they are most often geared toward philosophers and literary historians.
The person doing the most thoughtful theoretical work at the intersection of digital history and agent-based modeling is likely Marten Düring, who is definitely someone to keep an eye on if you’re interested in this area. An early innovator and strong practitioner in this field is Shawn Graham, who actively blogs about related issues. This technique, however, is far from the only one available to historians for devising experiments with the past. There’s a lot we can still learn from 17th century astronomers.