The Fallacy of the Right Answer

The Fallacy of the Right Answer is everywhere. With regards to education technology, it dates back at least to BF Skinner.

Skinner saw education as a series of definite, discrete, linear steps along a fixed, straight road; today this is called a curriculum. He referred to a child who guesses the password as “being right”. Khan Academy uses similar gatekeeping techniques in its exercises, limiting the context. Students must meet one criterion before proceeding to the next, being spoon-fed knowledge and seeing through a peephole not unlike Skinner’s machines. Furthermore, these steps are claimed to be objective, universal and emotionless. Paul Lockhart calls this the “ladder myth”, the conception of mathematics as a clear hierarchy of dependencies. But the learning hierarchy is tangled, replete with strange loops.

It is fallacious yet popular to think that a concept, once learned, is never forgotten. But most educated adults I know (including myself) find value in rereading old material, and make connections back to what they already have learned. What was once understood narrowly or mechanically can, when revisited, be understood in a larger or more abstract context, or with new cognitive tools. There are two words for “to know” in French. Savoir means to know a fact, while connaitre means to be familiar with, comfortable with, to know a person. The Right Answer loses sight of the importance, even the possibility, of knowing a piece of information like an old friend, to find pleasure in knowing, to know for knowing’s sake, because you want to. Linear teaching is workable for teaching competencies but not for teaching insights, things like why those mechanical methods work, how they can be extended, and how they can fail.

Symbol manipulation according to fixed rules is not cognition but computation. The learners take on the properties of the machines, and those who programmed them. As Papert observed, the computer programs the child, not the other way around (as he prefers). Much of this mechanical emphasis is driven by the SAT and other unreasonable standardized tests which are nothing more than timed high-stakes guessing games. They are gatekeepers to the promised land of College. Proponents of education reform frequently cite distinct age-based grades as legacy of the “factory line model” dating back to the industrial revolution. This model permeates not only how we raise children, but more importantly, what we raise them to do, what we consider necessary of an educated adult. Raising children to work machinery is the same as, or has given way to, raising them to work like machinery. Tests like the SAT emphasize that we should do reproducible de-individualized work, compared against a clear, ideal, unachievable standard. Putting this methodology online does not constitute a revolution or disruption.

patent_portrait

(source)

Futurists have gone as far to see the brain itself as programmable, in some mysteriously objective sense. At some point, Nicholas Negroponte veered off his illustrious decades-long path. Despite collaborating with Seymour Papert at the Media Lab, his recent work has been dropping tablets into rural villages. Instant education, just add internet! It’s great that the kids are teaching themselves, and have some autonomy, but who designed the apps they play with? What sort of biases and fallacies do they harbor? Do African children learning the ABCs qualify as cultural imperialism? His prediction for the next thirty years is even more troublesome: that we’ll acquire knowledge by ingesting it. Shakespeare will be encoded into some nano-molecular device that works its way through the blood-brain barrier, and suddenly: “I know King Lear!”. Even if we could isolate the exact neurobiological processes that constitute reading the Bard, we all understand Shakespeare in different ways. All minds are unique, and therefore all brains are unique. Meanwhile, our eyes have spent a few hundred million years of evolutionary time adapting to carry information from the outside world into our mind at the speed of an ethernet connection. Knowledge intake is limited not by perception but by cognition.

Tufte says, to simplify, add context. Confusion is not a property of information but of how it is displayed. He said these things in the context of information graphics but they apply to education as well. We are so concerned with information overload that we forget information underload, where our brain is starved for detail and context. It is not any particular fact, but the connections between them, that constitute knowledge.  The fallacy of reductionism is to insist that every detail matters: learn these things and then you are educated! The fallacy of holism is to say that no details matter: let’s just export amorphous nebulous college-ness and call it universal education! Bret Victor imagines how we could use technology to move from a contrived, narrow problem into a deeper understanding about generalized, abstract notions, much as real mathematicians do. He also presents a mental model for working on a difficult problem:

I’m trying to build a jigsaw puzzle. I wish I could show you what it will be, but the picture isn’t on the box. But I can show you some of the pieces… If you are building a different puzzle, it’s possible these pieces won’t mean much to you. You might not have a spot for them to fit, or you might not yet. On the other hand, maybe some of these are just the pieces you’ve been looking for.

One concern with Skinner’s teaching machines and their modern-day counterparts is that they isolate each student and cut off human interaction. We learn from each other, and many of the things that we learn fall outside of the curriculum ladder. Learning to share becomes working on a team; show-and-tell becomes leadership. Years later, in college, many of the most valuable lessons are unplanned, a result of meeting a person with very different ideas, or hearing exactly what you needed to at that moment. I found that college exposed to me brilliant people, and I could watch them analyze and discuss a problem. The methodology was much more valuable than the answer it happened to yield.

The hallmark of an intellectual is do create daily what has never existed before. This can be an engineer’s workpiece, an programmer’s software, a writer’s novel, a researcher’s paper, or an artist’s sculpture. None of these can be evaluated by comparing them to a correct answer, because the correct answer is not known, or can’t even exist. The creative intellectual must have something to say and know how to say it; ideas and execution must both be present. The bits and pieces of a curriculum can make for a good technician (a term I’ve heard applied to a poet capable of choosing the exact word). It’s not so much that “schools kill creativity” so much as they replace the desire to create with the ability to create. Ideally schools would nurture and refine the former (assuming something-to-say is mostly innate) while instructing the latter (assuming saying-it-well is mostly taught).

What would a society look like in which everyone was this kind of intellectual? If everyone is writing and drawing, who will take out the trash, harvest food, etc? Huxley says all Alphas and no Epsilons doesn’t work. Like the American South adjusting to an economy without slaves, elevating human dignity leaves us with the question of who will do the undignified work. As much as we say that every child deserves an education, I think that the creative intellectual will remain in an elite minority for years to come, with society continuing to run on the physical labor of the uneducated. If civilization ever truly extends education to all, then either we will need to find some equitable way of sharing the dirty work (akin to utopian socialist communes), or we’ll invent highly advanced robots. Otherwise, we may need to ask ourselves a very unsettling question: can we really afford to extend education to all, given the importance of unskilled labor to keep society running?

 


If you liked this post, you should go read everything Audrey Watters has written. She has my thanks.

Using Simulation

Sherry Turkle’s Simulation and its Discontents takes a disapproving stance towards software that mimics the real world. She surveys many fields of science and engineering, where simulation takes many forms, including protein folding, architectural drawings, and physical phenomena. She highlights how older practitioners miss having their hands on their workpiece, while younger ones are anxious about knowledge they may never have. In the 1980s, simulation drove a wedge between MIT faculty and students; more recently it has been begrudgingly accepted by all.

There is certainly a generation gap here, but it exists as much in the technology itself as in the minds of the scientists. Turkle repeatedly emphasizes the “black box” nature of software, and how its users cannot examine the code themselves. She conveniently ignores the open source movement, which creates software that can be understood, modified, and redistributed by its users. True, much of science and engineering software is still proprietary, and open source offerings are frequently inferior to the paid versions, but she doesn’t even have that discussion.

Secondly, even if we users could see the source, understanding it is not trivial. Her book predates the “learn to code” movement by a few years, but the same objections apply: computer science is an engineering field in its own right, and software should be developed and maintained by specialized practitioners rather than done “on the side” by all engineers. Yes, domain knowledge experts should be brought in when necessary. Research into more advanced programming languages will likely only make the situation worse, as they typically rely on an ever-larger and more abstract body of knowledge in order to work in them, thus catering to the expert over the beginner.

Any simulation that performs calculations that could be done by hand is really an automation. A true simulation, arguably by definition, cannot be fully understood. I agree that all engineers should be able to dash off a few lines to automate a menial task, but simulations are harder. In particular, there are languages (Python and Ruby) that are easy to learn well enough to automate simple tasks. But most simulations aren’t written in these languages. The need for performance drives simulations to be written in C or C++, frequently incorporating many libraries (shared code written by someone else; even programmers don’t understand all of their program). Unlike the command line utilities of yore, graphical user interfaces and high-performance graphics rendering require specialized and complex programming techniques. Integrating with the internet or a database makes programs more complicated still. Finally the size of programs has ballooned. There is so much code in a typical piece of software, and it is so complex, that I find it naive when non-programmers to insist that if only they could see the code, they could understand it.

Programs and programming today are far more complicated than in the 1980s. The most advanced climate models consist of a million lines of FORTRAN code, simulating equations from many disparate fields of natural science. They are beyond the understanding of any single individual. And indeed, understanding is no longer the goal. Just as technology has allowed us to see things invisible to our eyes, and hear things inaudible to our ears, simulation allows us to think things incomprehensible to our brains.

Turkle certainly grasps that deep, full understanding is imperiled, but only in her conclusion does she so much as entertain the notion that this may be necessary or good. Simulation may open up new possibilities more than it closes them. Science has surpassed the point of what can be understood by crystaline thought; the future is noisier and approximate. Robert Browning wrote that “a man’s reach should exceed his grasp”. A fictionalized Nikola Tesla ponders this quote in The Prestige, with the context that what man can affect, he cannot always control. Turkle would do well to heed his next remark: “It’s a lie. Man’s grasp exceeds his nerve.”

How do we get the nerve to embrace the new future of simulation? In part, by addressing specific concerns raised by Turkle’s interviewees. Defaults are too tempting, so we shouldn’t provide them. A design can appear finalized before it actually is, preventing further iteration, so displays can mimic physical imprecision. High-level summaries should allow the user to see examples, to jump between layers, to see why the computer has classified or calculated the way that it did. Nevertheless I expect changes in simulation to come primarily from software engineering and culture rather than the technology itself.

Turkle gives an example of a protein folding simulation devising a molecule that is clearly wrong to the biologist, because it defies her understanding of how proteins work. But what is her thought process if not another simulation? Perhaps it is more correct than the software in this case, but in general, thinking and outlining should be considered the cheapest, fastest, and lowest-fidelity of many simulation tools available to us. Intuition can be powerful, but it can also be wrong, and any claim in science or engineering requires more than intuition to support it. More formalized methods of thinking (how did they build skyscrapers in the 1920s?) are just following algorithms that the machine can do today, faster and with (potentially!) fewer errors. If the biologist can articulate her thought process, it can be automated, and if the cannot, it’s mere intuition.

With regards to creativity, simulation  and I include the word processor here — is a double-edged sword. When the barrier to creation is low, we can get our thoughts out quickly, and complete the dreaded first draft. Ideas and structure form and reflow on the fly.  The work is crafted interactively, iteratively, in a continuous and tight feedback loop. Human and simulation play off each other. This is clearly better than the computer working in isolation, such as the protein folding program that merely produced the “right” answer. What I propose is that it may also be superior to humans working “blind”, creating ideas fully in their heads, attempting to simulate them there, in discrete drafts. This model is a relic of hand- or typewritten pages, technologies where copying wasn’t instant. The downside is that it’s easy to do less thought before writing, and the end product may lack a harmonious global structure as a result. The compromise is to work with many tools of increasing fidelity and expense. When an idea does not work, we desire to “fail fast”  in the least expressive medium in which the flaw is manifest.

Frequently ideas we think are brilliant fall over when confronted, and a simulation can fail fast, can confront them quickly. In user interface design, paper prototypes are created not for the designer but for a test subject, and all the intellectualized beauty in the world means nothing if the user can’t operate the interface. This echoes a fundamental tenant of engineering across the board: you are not designing for yourself. What you think doesn’t matter unless validated by a ruthlessly objective third party. Writing now forms the exception: to reify human thought itself without the consent of the external world shifts the burden onto the author. Yet even though the writer struggles to crystalize his ideas as much as possible prior to composing them, he knows the value of trusted readers to provide feedback.

This leads us to the notion of software testing, which is all but absent from Turkle’s book. Provably correct software is an active area of research, so those shipping software today verify its correctness empirically. Testing exists on many scales, from system-wide routines of many actions to “unit tests” that cover as small a functionality as possible. Although typically written by the developers, a user can also benefit from writing unit tests, as they force her think about and articulate how the simulation would act in a very controlled instance. She will build confidence that the simulation matches her ideas. When a test fails, either the simulation, her understanding, or her ability to articulate her understanding, is incomplete.

Testing software is a philosophical shift from understanding it, moving programming away from science and towards engineering. Scientists will protest that experimenting on a designed thing is less effective than understanding the design directly, to which I respond by challenging the notion that software is designed. I have already discussed the staggering complexity of some software, and this should lead us to treat it as discovered rather than invented, and therefore investigated by experimentation rather than reverse-engineering. We explore simulation as an image of the real world: highly accurate but imperfect, yet faster, cheaper, and more versatile.

George Box, the statistician (what are statistics if not a simulation?), wrote that “all models are wrong, but some are useful”. He diffuses Turkle’s claim that simulations mislead by agreeing with it, and then salvages utility. He deconstructs and reconstructs simulation as a practice in a sentence. It is certainly worthwhile to make our models less wrong or more transparent when possible (these are often competing aims), and to supplement simulations with the real. Still, as in all discussions on culture-shaping technology, we must conclude that the main approach must be to use the technology more prudently, rather than ban it or hope that it will go away. Whether simulation acts as a magnifying glass or a funhouse mirror depend on how you look at it.

Threes and 2048

By now you know the story. Threes was developed by a small group of indie game developers over more than a year. They’ve posted a treasure trove of their development emails so anyone can see just how much work they went through. It shows: Threes has a very steep learning curve and cannot be beat by following a simple algorithm. Yet, the game is still fun to play. The cards have personality, increasingly so at higher values, and combined with the bright primary colors they create a laid-back, almost joyful atmosphere. The music — somehow — manages to capture the carefree “everyone loses eventually, so don’t take it too hard” aspect. All the subtle animations give the game emotional weight.

Not two weeks after Threes was released, a 19-year-old Italian web developer released 2048, which was actually based on an iOS app 1024 which marketed itself as “no need to pay for Threes”. 2048 is open source on GitHub, the de facto home of all recent, hip open source projects. Therefore it has spawned no end of derivatives and copy-cats. Most of these versions are meant to be funny, parodies, or even satiric. They are transient, gimmicky, and not all that fun. And yet, they are hackable. Tufts’s web development class had its students add a database-backed high score system. You could build a whole ecosystem of services related to 2048, all free and open source, malleable to anyone who knows how to code. And everyone knows how to code, right? And everyone wants nothing more in life that tile games, right?

Threes is like an Apple product: meticulously designed for functionality and emotion. The experience is open, with the bright white background and endlessly fun gameplay, but the implementation is closed, behind a paywall and with private source code. 2048 is like Linux: free, open source, but of lower quality. Sifting through a half dozen 2048 clones to find one you like and that doesn’t crash or spam you is remarkably similar to managing Linux software packages. 2048 is just numbers, and lacks the emotional charm from the music, animations, and voices of Threes.

Indeed, it’s possible to do fairly well in 2048 just by mindlessly shuffling tiles into a corner. This strategy is much less effective in Threes because the 1 and 2 cards get in the way. Threes also has a “next” indicator, telling you the color (but not value!) of the next card. This strikes the perfect balance: knowing the exact value would make the corner strategy workable, but knowing something allows the player to plan their next move. Being mindful of where cards are entering the board makes a tremendous difference in score. Threes is a game that rewards concentration; you have to think. 2048 is so easy a robot could do it.

The Threes developers spent a tremendous amount of time exploring every nook and cranny of the sliding numbered card concept. They eventually found a great game, hidden in the space of all possible games. But that space, as huge as it is, is actually fairly small. There’s only so many different choices you can make regarding core game mechanics, and yet it took months to explore. So when other people attempt to tackle much larger problems, like education or global poverty, without having even laid the groundwork that the Threes developers did, I’m extremely skeptical.

That said, much of what makes Threes stand out is the auxiliary material: art direction, sound, menus, the tutorial. And indeed that’s a lot of what the developers went back and forth on. Here is one except from their massive email archive:

But recently I’ve found myself thinking about the game as an exploration of identity. Like how the phrase “be yourself” is utter bullshit because “yourself” isn’t a thing that exists until you create it. We start out as just the sum of our nature+surroundings (1+2) and eventually we coalesce that into a sense of self that we can define and present to others. (3) And then eventually you get enough perspective to self reflect (3+3) and decide how to change.

And the metaphor actually holds up because your constantly learning about new ideas (1s) while dealing with genetics (2s) and also memories of how you dealt with stuff in the past (3s, 6s, 12s…) and really the trick is about how to line them all up and figure out how to grow.

I’d like to see that put together by a nineteen year-old in a weekend.

Prefer Verbs to Nouns

My principle, v0.2

Prefer verbs to nouns.

When Bret Victor introduced the concept of a principle, he said a good principle can be applied “in a fairly objective way”. This is the biggest problem with my first draft, which took several sentences to define what a powerful way of thinking was. A principle must be general enough to apply to many situations, but also able to operationalize to find meaning in any specific situation. Devising or crafting a principle requires inductive reasoning (specific to general), but applying it demands deductive reasoning (general to specific). Forging a principle resembles Paul Lockhart’s vision of mathematics: an idea that at first may be questioned and refined, but at some point begins “talk back”, instructing its creator rather than being shaped by it.

I could have formulated the principle as verbs, not nouns or similar, but the principle itself demands a verb. I have chosen prefer, but I fear that may not be active enough; something closer to choose or emphasize verbs over nouns may more fitting. As the principle predicts, identifying a dichotomy and even choosing one side is easy compared to selecting the verb to encompass the process and relationship. This principle retains status as a draft, although unlike its predecessor it does not have the glaring flaw of subjective application. The verb (and preposition serving it) are still to be determined, and the possibility of cutting a new principle from whole cloth also remains open.

All of this without a discussion of the principle itself! Human language is endlessly versatile and adaptive, and therefore (in hindsight!) it is quite fitting that I use the terms of language itself. Of course the principle does not apply specifically to language, but any field that involves structures and the relationships between them, which is to say, any field at all. It can apply to essays, presentations, or works of art. Finding the verbs and nouns of a particular field is often easy, even if it is difficult to abstract the process. With that said, verbs are not always grammatically verbs; -ing and -tion nouns can be fine verbs for the purpose of the principle.

The verbs should be emphasized to your audience, but the setting will determine how you craft their experience. Most of the liberal arts require grappling with verbs directly; a good thesis is architected around a verb that relates otherwise disparate observations or schools of thought. By emphasizing the verbs, one communicates causal mechanisms, transformations, relationships, and differences across time, location, demographics, and other variables. The goal is not merely to show that the nouns differ (“the a had x but the b had y”), but why, what acted on them to cause the differences. Frequently the base material (often historical events or written works) are already known to your audience, and you need to contribute more than just a summary. You need to justify a distinction.

However, in the presence of detailed, substructured, and numeric nouns, it is often best to let them speak directly. Often the evidence itself is novel, such as a research finding, and you want to present it objectively. In such cases, more frequent in science and engineering, placing your audience’s focus on verbs requires that you place yours on presenting the nouns. The more nouns you have, the more ways they can relate to each other; the more detailed the nouns, the more nuanced those relationships can be. When the nouns are shown correctly, your audience will have a wide array of verbs available to them; Edward Tufte gives examples (Envisioning Information, 50):

select, edit, single out, structure, highlight, group, pair, merge, harmonize, synthesize, focus, organize, condense, reduce, boil down, choose, categorize, catalog, classify, list, abstract, scan, look into, idealize, isolate, discriminate, distinguish, screen, pigeonhole, pick over, sort, integrate, blend, inspect, filter, lump, skip, smooth, chunk, average, approximate, cluster, aggregate, outline, summarize, itemize, review, dip into, flip through, browse, glance into, leaf through, skim, refine, enumerate, glean, synopsize, winnow the wheat from the chaff, and separate the sheep from the goats

The ability to act in these ways is fragile.  Inferior works destroy verb possibilities (science and engineering) or never present them at all (liberal arts). Verbs are the casualties of PowerPoint bullets; nouns can often be picked out from the shrapnel but the connections between them are lost. But conversely, a focus on verbs promotes reason and the human intellect. Verbs manifest cognition and intelligence. Emphasizing verbs is a proxy and litmus test for cogent thought.

Type:Rider and Flappy Bird

I wouldn’t have thought typography could be the subject of a video game, but Type:Rider does just that. The levels are a tour of Western history from the middle ages onward, each corresponding to a different typeface in the context of its era. The Gothic type’s levels take cues from medieval churches while the 1920’s Futura feels like a modern art museum. The player’s avatar is a colon, two rolling dots bound together by some magnetic-seeming attraction. Gameplay consists of navigating through terrain including each letter of the alphabet rendered in the that typeface. The letters are arranged to create interesting geometrical puzzles that make them memorable. The player also navigates through oversized versions of the printing technologies of the day, meanwhile collecting asterisks that unlock brief passages about the key figures and inventions of the time period.

There are a number of features that make Type:Rider stand out. It is highly polished, with beautiful visual environments and suitable thematic music. (Surprisingly the typesetting of the informative passages is often found wanting; perhaps the English translation wasn’t proofed by the original European developers?) The controls are relatively expressive, in that with a few taps the skilled player can move the colon in one of many possible ways. The game has value: it took a team of experienced designers and developers time and money to create it, and the user must expend time and money to enjoy it. But yet, the game has a deeper message. Yes, it’s about typography, but mere type is the means by which we transfer knowledge; typography is the beatification of knowledge. Typography rewards diligence, attention to detail, graphical density, and knowledge of prior work. Typography is the wings on which intellectualism is borne.

Contrast this with the maddeningly weak and imprecise wings of Flappy Bird. Wired does a good job recounting the saga of the infamous iOS game and its creator, Dong Nguyen. Anyone can pick up the game and play it immediately, but playing well is exceedingly difficult: mastery and skill-building are sacrificed on the alter of ease-of-use. Play happens in all-too-brief bouts, which provide instant gratification with no time commitment. No depth of knowledge, skill, or artistic message is ever accumulated.

Papert distinguishes between children programming computers and computers programming children, and this is certainly the latter. Flappy bird conditions one exact response, with no room for exploration or creativity. No justification is given as to why the world must be the way it so firmly is. More concretely, flappy bird is fake difficulty, riding on an artificially narrow method of control. It perniciously makes the smart phone, and the human, less smart.

Dong Nguyen made (and is likely still making) fifty thousand dollars a day off advertising shown to the game’s users. I highly doubt the users (largely teens) are spending anywhere close to that amount of money on the advertised products. Flappy bird generates money but not wealth; like doomed financial products it is built on value that simply isn’t there. Sooner or later, this bubble must burst.

But despite the attention directed towards Flappy bird, it is hardly unique. Only four of the top fifty grossing apps (as of when I checked) are not games (Pandora, Skype, and two dating apps). The rest are games, targeted at the under-20 crowd, driven by ads and in-app purchases (which include the removal of ads). The app store has become Western kids in a gigantic candy store, and this has pushed adults and their fine intellectual cuisine off to the margins. The market has spoken: mass-produced low-quality ad-ridden software for entitled children is what sells, adults and society be damned.

I will quote (again) from Jaron Lanier, You Are Not A Gadget: “Rooms full of MIT PhD engineers [are] not seeking cancer cures or sources of safe drinking water for the underdeveloped world but schemes to send little digital pictures of teddy bears and dragons between adult members of social networks. At the end of the road of the pursuit of technological sophistication appears to lie a playhouse in which human kind regresses to nursery school.”

Even Type:Rider is not immune. It has the requisite Facebook and Twitter integration, though they are less prominent. It is also available as a Facebook game. What is offers, then, is not a completely pure solitary experience but rather a compromise given the nature of the market.

It is said that technology changes quickly and people change slowly, but the reality is more complex. People have shown a remarkable ability to adapt to new technologies, without fundamentally altering how they think or what goals they have. Meanwhile, the face of technology changes, but many ideas remain timeless and fixed, old wine repackaged into new bottles. Furthermore standards and protocols by which devices communicate with each other, once set, become incredibly difficult to change. We are in danger of not changing with technology, and then creating technology that prevents us from changing.

Of Signs, Skyscrapers, and Software

I was looking for a book on data visualization. Having gone through Edward Tufte’s classics, I browsed the Tufts library catalog by “visualization”. The two keepers were only tangentially related, but I’ve learned that I sometimes attack problems too directly, so I checked out Signage and Wayfinding Design by Chris Calori and The Heights: Anatomy of a Skyscraper by Kate Ascher. Both have involve service through the built environment. That is, unlike the social justice programs that many of my classmates engage in that serve interpersonally, these books see service conducted through the proxy of a created object. This model appeals to me because the object can serve many more people than I could personally interact with. The object endures to serve in the future, as opposed to the many charitable acts that have vanishingly immediate returns. That is, service through objects is more efficient.

In the case of signage, it is intellectual service. Wayfinding provides empowering knowledge much the way that software can (should). Signage also serves a secondary placemaking role. Signs not only describe the built environment; they are part of it; they should embody emotions, preferring to lend character to its environment over abstract minimalism. Therefore, signs walk the same tightrope that infographics do, between information clarity and contextual aesthetics.

Chris Calori leaves no stone unturned as she documents the full process of signage production. On one hand, it is ruthlessly physical, with details such as mounting, dimensions, lighting, materials, and finishes to be specified prior to fabrication. Much of the waterfall-like process is principled on preventing the full construction of a faulty signage program, by using detailed plans, miniatures, prototypes, and renderings. On the other hand, signage is a great example of the divide between art and design. Art attempts to communicate non-obvious truths through subtety that takes time to absorb. Signage (and design) is just the opposite: communicate rather mundane information quickly and unambiguously. Calori defines the pyramid model of signage, which encompasses the information content, the graphics, and the hardware. Design subdivides into the abstract information, concerned with hierarchies and placement, and graphics, concerned with symbols, diagrams, and typefaces. The book thoroughly addresses each of these, as well as the regulatory and legal concerns one is likely to encounter along the way. It also includes thirty-two pages of color photographs of finished signage programs, which are not to be missed.

As for The Heights, the book itself is intellectual service. Printed in full color, it’s a surprisingly detailed-yet-accessible look at the engineering and planning behind tall buildings. Want to know about cranes, air handlers, curtain walls, elevator safety, green roofs, fire sprinklers, floor plates, pile drivers, and window washers? It’s all there, with helpful illustrations. Section titles are printed in a 3D extruded typeface that resembles a building (for once, a justified use case) and the table of contents is done delightfully as an elevator directory, reading from bottom to top. Wayfinding is not mentioned in the text, but its principles are applied to the presentation itself.

Of course, the very act of creating a well-designed skyscraper contributes tremendously to the built environment. Such a building can provide living and working space for thousands of people for a century. Design decisions can become crucially important or confining decades after they were made. Unlike Calori’s laser-focus, the skyscrapers involve thousands of people of diverse education and wealth backgrounds, from construction workers to financiers, tenants to janitors. Construction on this scale is an act with huge societal ramifications. Engineering is not neutral, politically, socially, or ethically.

But the authors are undaunted. They strive to make objects, whether signs or skyscrapers, that make life more enjoyable, more comprehensible, and more fair for all who come into contact with them. Through technical competence, goal-oriented design, hard work, and luck, objects large and small come to enrich our lives. What kind of object do you want to make?

Infographics and Data Graphics

I’d like to set the record straight about two types of graphical documents floating around the internet. Most people don’t make a distinction between infographics and data graphics. Here are some of each – open them in new tabs and see if you can tell them apart.

No peeking!

No, really, stop reading and do it. I can wait.

Okay, had a look and made your categorizations? As I see it, dog food, energy, and job titles are infographics, and Chicago buildings, movie earnings, and gay rights are data graphics. Why? Here are some distinctions to look for, which will make much more sense now that you’ve seen some examples. Naturally these are generalizations and some documents will be hard to classify, but not as often as you might think.

Infographics emphasize typography, aesthetic color choice, and gratuitous illustration.
Data graphics are pictorially muted and focused; color is used to convey data.

Infographics have many small paragraphs of text communicate the information.
Data graphics are largely wordless except for labels and an explanation of the visual encoding.

In infographics, numeric data is scant, sparse, and piecemeal.
In data graphics, numeric data is plentiful, dense, and multivariate.

Infographics have many components that relate different datasets; sectioning is used.
Data graphics have single detailed image, or less commonly multiple windows into the same data.

An infographic is meant to be read through sequentially.
A data graphic is meant to be scrutinized for several minutes.

In infographics, the visual encoding of numeric information is either concrete (e.g. world map, human body), common (e.g. bar or pie charts), or nonexistent (e.g. tables).
In data graphics, the visual encoding is abstract, bespoke, and must be learned.

Infographics tell a story and have a message.
Data graphics show patterns and anomalies; readers form their own conclusions.

You may have heard the related term visualization – a data graphic is a visualization on steroids. (An infographic is a visualization on coffee and artificial sweetener.) A single bar, line, or pie chart is most likely a visualization but not a data graphic, unless it takes several minutes to absorb. However, visualizations and infographics are both generated automatically, usually by code. It should be fairly easy to add new data to a visualization or data graphic; not so for infographics.

If you look at sites like visual.ly which collects visualizations of all stripes, you’ll see that infographics far outnumber data graphics. Selection bias is partially at fault. Data graphics require large amounts of data that companies likely want to keep private. Infographics are far better suited to marketing and social campaigns, so they tend to be more visible. Some datasets are better suited to infographics than data graphics. However, even accounting for those facts, I think we have too many infographics and too few data graphics. This is a shame, because the two have fundamentally different worldviews.

An infographic is meant to persuade or inspire action. Infographics drive an argument or relate a story in a way that happens to use data, rather than allowing the user to infer more subtle and multifaceted meanings. A well-designed data graphic can be an encounter with the sublime. It is visceral, non-verbal, profound; a harmony of knowledge and wonder.

Infographics already have all the answers, and serve only to communicate them to the reader. A data graphic has no obvious answers, and in fact no obvious questions. It may seem that infographics convey knowledge, and data graphics convey only the scale of our ignorance, but in fact the opposite is true. An infographic offers shallow justifications and phony authority; it presents that facts as they are. (“Facts” as they “are”.) A data graphic does not foster any conclusion upon its reader, but at one level of remove, provides its readers with tools to draw conclusions. Pedagogically, infographics embrace the fundamentally flawed idea that learning is simply copying knowledge from one mind to another. Data graphics accept that learning is a process, which moves from mystery to complexity to familiarity to intuition. Epistemologically, infographics ask that knowledge be accepted on little to no evidence, while data graphics encourage using evidence to synthesize knowledge, with no prior conception of what this knowledge will be. It is akin to memorizing a fact about the world, or accepting the validity of the scientific method.

However, many of the design features that impart data graphics with these superior qualities can be exported back to infographics, with compelling results. Let’s take this example about ivory poaching. First off, it takes itself seriously: there’s no ostentatious typography and the colors are muted and harmonious. Second, subject matter is not a single unified dataset but multiple datasets that describe a unified subject matter. They are supplemented with non-numeric diagrams and illustrations, embracing their eclectic nature. Unlike most infographics, this specimen makes excellent use of layout to achieve density of information. Related pieces are placed in close proximity rather than relying on sections; the reader is free to explore in any order. This is what an infographic should be, or perhaps it’s worthy of a different and more dignified name, information graphic. It may even approach what Tufte calls “beautiful evidence”.

It’s also possible to implement a data graphic poorly. Usually this comes down to a poor choice of visual encoding, although criticism is somewhat subjective. Take this example of hurricanes since 1960. The circular arrangement is best used for months or other cyclical data. Time proceeds unintuitively counterclockwise. The strength of hurricanes is not depicted, only the number of them (presumably – the radial axis is not labeled!). The stacked bars make it difficult to compare hurricanes from particular regions. If one wants to compare the total number of hurricanes, one is again stymied by the polar layout. Finally, the legend is placed at the bottom, where it will be read last. Data graphics need to explain their encoding first; even better is to explain the encoding on the diagram itself rather than in a separate legend. For example, if the data were rendered as a line chart (in Cartesian coordinates), labels could be placed alongside the lines themselves. (Here is a proper data graphic on hurricane history.)

An infographic typically starts with a message to tell, but designers intent on honesty must allow the data to support their message. This is a leap of faith, that their message will survive first contact with the data. The ivory poaching information graphic never says that poaching is bad and should be stopped, in such simple words. Rather it guides us to that conclusion without us even realizing it. Detecting bias in such a document becomes much more difficult, but it also becomes much more persuasive (for sufficiently educated and skeptical readers). Similarly, poor data graphics obscure the data, either intentionally because they don’t support the predecided message, or unintentionally because of poor visual encoding. In information visualization, as in any field, we must be open to the hard process of understanding the truth, rather than blithely accepting what someone else wants us to believe.

I know which type of document I want to spend my life making.

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