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Descriptions Aren’t Prescriptions

Reading Time: 7 minutes

When we look at a representation of reality, we can choose to either see it as descriptive, meaning it tells us what the world is currently like, or as prescriptive, meaning it tells us how the world should be. Descriptions teach us, but they also give us room to innovate. Prescriptions can get us stuck. One place this tension shows up is in language.

In one chapter of The Utopia of Rules: On Technology, Stupidity, and the Secret Joys of Bureaucracy, David Graeber describes his experience of learning Malagasy, the national language of Madagascar. While the language’s writing system came about in the fifteenth century, it wasn’t until the early nineteenth century that missionaries documented the rules of Malagasy grammar for the purpose of translating scripture.

Of course, the “rules” of Malagasy the missionaries recorded weren’t rules at all. They were reflections of how people spoke at that point in time, as far as outside observers could tell. Languages don’t usually come into existence when someone invents the rules for them. Instead, languages evolve and change over time as speakers make modifications or respond to new needs.

However, those early nineteenth-century records remained in place as the supposed “official” version of Malagasy. Children learned the old form of grammar in school, even as they spoke a somewhat different version of the language at home. For Graeber, learning to speak the version of Malagasy people actually understood in conversation was a challenge. Native speakers he hired would instruct him on the nineteenth-century grammatical principles, then turn and speak to each other in a whole other fashion.

When asked why they couldn’t teach him the version of the language they spoke, Graeber’s Malagasy teachers responded that they were just using slang. Asked why no one seemed to speak the official version, they said people were too lazy. Graeber writes, “Clearly the problem was that the entire population had failed to memorize their lessons properly. But what they were actually denying was the legitimacy of collective creativity, the free play of the system. ” While the official rules stayed the same over the decades, the language itself kept evolving. People assumed the fault of not speaking “proper” Malagasy lay with them, not with the outdated dictionary and grammar. They confused a description for a prescription. He writes:

It never seems to occur to anyone—until you point it out—that had the missionaries came and written their books two hundred years later, current usages would be considered the correct ones, and anyone speaking as they had two hundred years ago would themselves be assumed to be in error.

Graeber sees the same phenomenon playing out in other languages for which grammars and dictionaries only came into existence a century or two ago. Often, such languages were mostly spoken and, like Malagasy, no one made formal records until the need arose for people from elsewhere to make translations. Instead of treating those records as descriptive and outdated, those teaching the language treat them as prescriptive—despite knowing they’re not practical for everyday use.

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Why don’t people talk “proper”?

So why can’t people just speak a language per the official rules? If someone has gone to all the effort of identifying and recording the rules and people received instruction on them in school, why not follow them? Why keep changing things up?

If languages didn’t evolve, it would make life a lot easier for historians looking at texts from the past. It would also simplify matters for people learning the language, for those coming from different areas, and even for speakers across generations. Yet all languages change all the time.

Graeber suggests the reason for this is because people like to play. We find it dull to speak according to the official rules of our language. We seek out novelty in our everyday lives and do whatever it takes to avoid boredom. Even if each person only plays a little bit once in a while, the results compound. Graeber explains that “this playing around will have cumulative effects.”

Languages still need conventions so people can understand each other. The higher the similarity between the versions of a language different people speak, the more they can communicate. At the same time, they cannot remain rigid. Trying to follow an unyielding set of strict rules will inevitably curtail the usefulness of a language and prevent it from developing in interesting and necessary ways. Languages need a balance: enough guidance to help everyone understand each other and provide an entry point for learners, and enough flexibility to keep updating the rules as actual usage changes.

As a result, languages call into question our idea of freedom: “It’s worth thinking about language for a moment, because one thing it reveals, probably better than any other example, is that there is a basic paradox in our very idea of freedom. On the one hand, rules are by their nature constraining. Speech codes, rules of etiquette, and grammatical rules, all have the effect of limiting what we can and cannot say. ” On the other hand, no rules whatsoever mean no one can understand each other.

Languages need frameworks, but no amount of grammar classes or official dictionaries will prevent people from playing and having fun with their speech.

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The dictionary is not the language

“The map is not the territory” means that any representation of reality has to be a simplification that may contain errors, become outdated, or reflect biases. Maps remove details that aren’t necessary for their intended use. Representations of complex systems may show expected behavior or ideal behavior. For example, the London Underground map doesn’t reflect the distances between stations because this information isn’t important to most commuters. If a map represented its territory without reducing anything, it would be identical to the territory and therefore would be useless. In fact, the simplest maps can be the most useful because they’re the easiest to understand and remember.

Sometimes maps are descriptive, and sometimes they’re prescriptive; often they’re a bit of both. We run into problems when we confuse one type for another and try to navigate an idealized territory or make the real territory fit an idealized image.

A language’s grammar and dictionary are a sort of map. They take a complex system—a language spoken by what could be tens of millions of people—and aim to represent it with something which is, by comparison, simple. The official rules are not the language itself, but they provide guidance for navigating it. Much like a map of a city needs periodic updates as parts are torn down, built up, renamed, destroyed, added, and so on, the official rules need updating as the language changes. Trying to learn Malagasy using grammar rules written two hundred years ago is like trying to navigate Antananarivo using a street map made two hundred years ago.

A map of a complex system, like a language, is meant to help us find our way by giving us a sense of how things looked at one point in time—it’s usually descriptive. It doesn’t necessarily tell us how that system should look, and we may run into problems if we try to make it conform to the map, ignoring the system’s own adaptive properties. Even if the cartographer never intended this, we can end up treating a map as a prescription. We try to make reality conform to the map. This is what occurs with languages. Graeber calls this the “grammar-book effect”:

People do not invent languages by writing grammars, they write grammars—at least, the first grammars to be written for any given language—by observing the tacit, largely unconscious rules that people seem to be employing when they speak. Yet once a book exists, and especially once it is employed in schoolrooms, people feel that the rules are not just descriptions of how people do talk, but prescriptions for how they should talk.

As we’ve seen, one reason the map is not the territory with language is because people feel compelled to play and experiment. When we encounter representations of systems involving people, we should keep in mind that while we may need rules for the sake of working together and understanding each other, we’re always pushing up against and reshaping those rules. We find it boring to follow a rigid prescription.

For instance, imagine some of the documents you might receive upon starting a role at a new company. Process documents showing step by step how to do the main tasks you’ll be expected to perform. But when the person you’re replacing shows you how to do those same tasks, you notice they don’t follow the listed steps at all. When you ask why, they explain that the process documents were written before they started actually carrying out those tasks, meaning they discovered more efficient ways afterward.

Why keep the process documents, then? Because for someone filling in or starting out, it might make sense to follow them. It’s the most defensible option. Once you truly know the territory and won’t change something without considering why it was there in the first place, you can play with the rules. Those documents might be useful as a description, but they’re unlikely to remain a prescription for long.

The same is true for laws. Sometimes aspects of them are just descriptive of how things are at one point in time, but we end up having to keep following them to the letter because they haven’t been updated. A law might have been written at a time when documents needed sending by letter, meaning certain delays for shipping. Now they can be sent by email. If the law hasn’t been updated, those delay allowances turn from descriptions into prescriptions. Or a law might reflect what people were permitted to do at the time, but now we assume people should have the right to do that thing even if we have new evidence it’s not the best idea. We are less likely to change laws if we persist in viewing them as prescriptive.

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Conclusion

Descriptions of reality are practical for helping us navigate it, while also giving us room to change things. Prescriptions are helpful for giving us ways of understanding each other and providing enough structure for shared conventions, but they can also become outdated or end up limiting flexibility. When you encounter a representation of something, it’s useful to consider which parts are descriptive and which parts are prescriptive. Remember that both prescriptions and descriptions can and should change over time.

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The FS team were saddened to hear of David Graeber’s passing, shortly after we completed this article. We hope his books will continue to inspire and educate new readers for many years to come.

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What Sharks Can Teach Us About Survivorship Bias

Reading Time: 7 minutes

Survivorship bias refers to the idea that we get a false representation of reality when we base our understanding only on the experiences of those who live to tell their story. Taking a look at how we misrepresent shark attacks highlights how survivorship bias distorts reality in other situations.

When asked what the deadliest shark is to humans, most people will say the great white. The lasting influence of the movie Jaws, reinforced by dozens of pop culture references and news reports, keeps that species of shark at the top of the mind when one considers the world’s most fearsome predators. While it is true that great white sharks do attack humans (rarely), they also leave a lot of survivors. And they’re not after humans in particular. They usually just mistake us for seals, one of their key food sources.

We must be careful to not let a volume of survivors in one area blind us to the stories of a small number of survivors elsewhere. Most importantly, we need to ask ourselves what stories are not being told because no one is around to tell them. The experiences of the dead are necessary if we want an accurate understanding of the world.

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Before we drill down into some interesting statistics, it’s important to understand that great whites are one member of a class of sharks with many common characteristics. Great whites are closely related to tiger and bull sharks. They all have similar habitats, physiology, and instincts. They are also all large, with an average size over ten feet long.

Tiger and bull sharks rarely attack humans, and to someone being bit by one of these huge creatures, there isn’t all that much difference between them. The Florida Museum’s International Shark Attack file explains that “positive identification of attacking sharks is very difficult since victims rarely make adequate observations of the attacker during the ‘heat’ of the interaction. Tooth remains are seldom found in wounds and diagnostic characters for many requiem sharks [of which the great white is one] are difficult to discern even by trained professionals.”

The fatality rate in known attacks is 21.5% for the bull shark, 16% for the great white, and 26% for the tiger shark. But in sheer volume, attacks attributed to great whites outnumber the other two species three to one. So there are three times as many survivors to tell the story of their great white attack.

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When it comes to our picture of reality of the most dangerous shark, there are other blind spots. Not all sharks have the same behaviors as those three, such as swimming close to shore and being around enough prey to develop a preference for fat seals versus bony humans. Pelagic sharks live in the water desert that is the open ocean and have to eat pretty much whatever they can find. The oceanic white tip is a pelagic shark that is probably far more dangerous to humans—we just don’t come into contact with them as often.

There are only fifteen documented attacks by an oceanic white tip, with three of those being fatal. But since most attacks occur in the open ocean in more isolated situations (e.g., a couple of people on a boat versus five hundred people swimming at a beach), we really have no idea how dangerous oceanic white tips are. There could be hundreds of undocumented attacks that left behind no survivors to tell the tale.

One famous survivor story gives us a glimpse of how dangerous oceanic white tips might be. In 1945, a Japanese submarine shot down the USS Indianapolis. For a multitude of reasons, partly due to the fact that the Indianapolis was on a top secret mission and partly due to tragic incompetence, a rescue ship was not sent for four days. Those who survived the ship’s sinking had to then try to survive in the open ocean with little gear until rescue arrived. The water was full of sharks.

In Indianapolis: The True Story of the Worst Sea Disaster in US Naval History and the Fifty-Year Fight to Exonerate an Innocent Man, Lynn Vincent and Sara Vladic quote Boatswain’s Mate Second Class Eugene Morgan as he described part of his experience: “All the time, the sharks never let up. We had a cargo net that had Styrofoam things attached to keep it afloat. There were about fifteen sailors on this, and suddenly, ten sharks hit it and there was nothing left. This went on and on.” These sharks are believed to have been oceanic white tips. It’s unknown how many men died from shark attacks. Many also perished due to exposure, dehydration, injury, and exhaustion. Of the 1,195 crewmen originally aboard the ship, only 316 survived. It represents the single biggest loss of life from a single ship in US naval history.

Because humans are rarely in the open ocean in large numbers, not only are attacks by this shark less common, there are also fewer survivor stories. The story of the USS Indianapolis is a rare, brutal case that provides a unique picture.

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Our estimation of the shark that could do us the most harm is often formed by survivorship bias. We develop an inaccurate picture based on the stories of those who live to tell the tale of their shark attack. We don’t ask ourselves who didn’t survive, and so we miss out on the information we need to build an accurate picture of reality.

The point is not to shift our fear to oceanic white tips, which are, in fact, critically endangered. Our fear of sharks seems to make us indifferent to what happens to them, even though they are an essential part of the ocean ecosystem. We are also much more of a danger to sharks than they are to us. We kill them by the millions every year. Neither should we shift our fear to other, more lethal animals, which will likely result in the same indifference to their role in the ecosystem.

The point is rather to consider how well you make decisions when you only factor in the stories of the survivors. For instance, if you were to try to reduce instances of shark attacks or try to limit their severity, you will not likely get the results you are after if you only pay attention to the survivor stories. You need to ask who didn’t make it and try to figure out their stories as well. If you try to implement measures aimed only at great whites near beaches, your measures might not be effective against other predatory sharks. And if you conclude that swimmers are better off in the open ocean because sharks seem to only attack near beaches, you’d be completely wrong.

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Survivorship bias crops up all over our lives and impedes us from accurately assessing danger. Replace “dangerous sharks” with “dangerous cities” or “dangerous vacation spots” and you can easily see how your picture of a certain location might be skewed based on the experiences of survivors. We can’t be afraid of a tale if no one lives to tell it. More survivors can make something seem more dangerous rather than less dangerous because the volume of stories makes them more memorable.

If fewer people survived shark attacks we wouldn’t have survivor stories influencing our perception about how dangerous sharks are. In all likelihood we would attribute some of the ocean deaths to other causes, like drowning, because it wouldn’t occur to us that sharks could be responsible.

Understanding survivorship bias prompts us to look for the stories of those who weren’t successful. A lack of visible survivors with memorable stories might mean we view other fields as far safer and easier than they are.

For example, a field of business where people who experience failures go on to do other things might seem riskier than one where people who fail are too ashamed to talk about it. The failure of tech start-ups sometimes feels like daily news. We don’t often, however, hear about the real estate agent who has trouble making sales or who keeps getting outbid on offers. Nor do we hear much about architects who design terrible houses or construction companies who don’t complete projects.

Survivorship bias prompts us to associate more risk with industries that exhibit more public failures. But the failures from industries or businesses that aren’t shared are equally important. If we focus only on the survivor stories, we might think that being a real estate agent or an architect is safer than starting a technology company. It might be, but we can’t only base our understanding on which career option is the best bet on the widely shared stories of failure.

If we don’t factor survivorship bias into our thinking we end up in a classic map is not the territory problem. The survivor stories become a poor navigational tool for the terrain.

Most of us know that we shouldn’t become a writer based on the results achieved by J.K Rowling and John Grisham. But even if we go out and talk to other writers, or learn about their careers, or attend writing seminars given by published authors, we are still only talking to the survivors.

Yes, it’s super inspiring to know Stephen King got so many rejections early in his career that the stack of them was enough to pull a nail out of the wall. But what about the writers who got just as many rejections and never published anything? Not only can we learn a lot from them about the publishing industry, we need to consider their experiences if we want to anticipate and understand the challenges involved in being a writer.

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Not recognizing survivorship bias can lead to faulty decision making. We don’t see the big picture and end up optimizing for a small slice of reality. We can’t completely overcome survivorship bias. The best we can do is acknowledge it, and when the stakes are high or the result important, stop and look for the stories of those who were unsuccessful. They have just as much, if not more, to teach us.

The next time you’re assessing risk, ask yourself: am I paying too much attention to the great white sharks and not enough to the oceanic white tips?

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Why Read? Advice From Harold Bloom

Reading Time: 6 minutes

The late Harold Bloom, literary critic and professor, may well have been one of the most prolific readers of all time. Given that, Bloom was uniquely well positioned to answer the question of why we should read and how we should go about it.

According to legend, Bloom could read a 400-page book in an hour without sacrificing comprehension and could recite the whole of Shakespeare’s poetry by heart. He was also a prodigious writer, producing over fifty books during his lifetime, as well as editing hundreds of anthologies.

In How to Read and Why, Bloom dispenses wisdom for the avid reader. In this article, we’ll share some of the most striking advice from the book on… well, how to read and why.

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Introduction

The most healing of pleasures

“Reading well is one of the great pleasures that solitude can afford you, because it is, at least in my experience, the most healing of pleasures. It returns you to otherness, whether in yourself or in friends, or in those who may become friends. Imaginative literature is otherness, and as such alleviates loneliness. We read not only because we cannot know enough people, but because friendship is so vulnerable, so likely to diminish or disappear, overcome by space, time, imperfect sympathies, and all the sorrows of familial and passional life.”

The value of irony

“Irony demands a certain attention span and the ability to sustain antithetical ideas, even when they collide with one another. Strip irony away from reading, and it loses at once all discipline and all surprise. Find now what comes near to you, that can be used for weighing and considering, and it will very likely be irony, even if many of your teachers will not know what it is, or where it is to be found.”

Why read?

“We read deeply for varied reasons, most of them familiar: that we cannot know enough people profoundly enough; that we need to know ourselves better; that we require knowledge, not just of self and others, but of the way things are. Yet the strongest, most authentic motive for deep reading of the now much-abused traditional canon is the search for a difficult pleasure.

. . . I urge you to find what truly comes near to you, that can be used for weighing and considering. Read deeply, not to believe, not to accept, not to contradict, but to learn to share in that one nature that writes and reads.”

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Chapter 1: Short Stories

How to read short stories

“Short stories favor the tacit; they compel the reader to be active, and to discern explanations that the writer avoids. The reader, as I have said before, must slow down, quite deliberately, and start listening with the inner ear. Such listening overhears the characters, as well as hearing them; think of them as your characters, and wonder at what is implied, rather than told about them. Unlike most figures in novels, their foregrounding and postgrounding are largely up to you, utilizing the hints subtly provided by the writer.”

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Chapter 2: Poems

How to read poems

“. . . Wherever possible, memorize them. . . . Silent intensive rereadings of a shorter poem that truly finds you should be followed by recitations to yourself until you discover that you are in possession of the poem. . . . Committed to memory, the poem will possess you, and you will be able to read it more closely, which great poetry demands and rewards.”

Why read poetry?

“Only rarely can poetry aid us in communicating with others; that is beautiful idealism, except at certain strange moments, like the instant of falling in love. Solitude is the more frequent mark of our condition; how shall we people that solitude? Poems can help us to speak to ourselves more clearly and more fully, and to overhear that speaking. . . . We speak to an otherness in ourselves, or to what may be best and oldest in ourselves. We read to find ourselves, more fully and more strange than otherwise we could hope.”

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Chapter 3: Novels, Part 1

The difference between novels and poetry

“In some respects, reading a novel ought not to differ much from reading Shakespeare or reading a lyric poem. What matters most is who you are, since you cannot evade bringing yourself to the act of reading. Because most of us also bring definite expectations, a difference enters with the novel, where we think to encounter, if not our friends and ourselves, then a recognizable social reality, whether contemporary or historical.

. . . Novels require more readers than poems do, a statement so odd that it puzzles me, even as I agree with it. Tennyson, Browning, and Robert Frost had large audiences, but perhaps did not need them. Dickens and Tolstoy had vast readerships, and needed them; multitudes of overhearers are built into their art. How do you read a novel differently if you suspect you are one of a dwindling elite rather than the representative of a great multitude?”

Why read Don Quixote?

“Reading Don Quixote is an endless pleasure, and I hope I have indicated some aspects of how to read it. We are, many of us, Cervantine figures, mixed blends of the Quixotic and the Panzaesque. . . . It remains the best as well as the first of all novels, just as Shakespeare remains the best of all dramatists. There are parts of yourself you will not know fully until you know, as well as you can, Don Quixote and Sancho Panza.”

How to read Great Expectations

“With the deepest elements in one’s own fears, hopes, and affections: to read as if one could be a child again. Dickens invites you to do so, and makes it possible for you; that may be his greatest gift. Great Expectations does not take us into the Sublime, as Shakespeare and Cervantes do. It wants to return us to origins, painful and guilty as perhaps they must be. The novel’s appeal to our childlike need for love, and recovery of the self, is nearly irresistible. The “why” of reading it is then self-evident: to go home again, to heal our pain.”

A question to ask of great novels

“Do the principal characters change and, if they do, what causes them to change?”

Again, why read?

“The ultimate answer to the question “Why read?” is that only deep, constant reading fully establishes and augments an autonomous self. Until you become yourself, what benefit can you be to others?”

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Chapter 4: Plays

Why read Hamlet?

“Because, by now, this play makes us an offer we cannot refuse. It has become our tradition, and the word our there is enormously inclusive. Prince Hamlet is the intellectual’s intellectual: the nobility, and the disaster, of Western consciousness. Now Hamlet has also become the representation of intelligence itself, and that is neither Western nor Eastern, male nor female, black nor white, but merely the human at its best, because Shakespeare is the first truly multicultural writer.”

How to read Shakespeare

“Reading Shakespeare’s plays, you learn to meditate upon what is left out. That is one of the many advantages that a reader has over a theatergoer in regard to Shakespeare. Ideally, one should read a Shakespeare play, watch a good performance of it, and then read it again. Shakespeare himself, directing his play at the Globe, must have experienced discomfort at how much a performance had to neglect, though we have no evidence of this. However instructed by Shakespeare, it is difficult to imagine the actor Richard Burbage catching and conveying all of Hamlet’s ironies, or the clown Will Kemp encompassing the full range of Falstaff’s wit in the Henry IV plays.”

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Conclusion

At FS, we often talk about the benefits of reading as a way of learning from the experiences of others and avoiding mistakes. But, as Bloom shows us, the benefits are not just about becoming smarter and more productive.

Reading can help us alleviate loneliness and get to know more people on an intimate level than we could otherwise. It can provide greater self-knowledge, as the words of others give us a lens for understanding ourselves. As a “difficult pleasure,” the ways in which books challenge us help us to grow. Wrestling with a text teaches us a great deal about our capabilities and our values. There is also immense satisfaction and increased confidence when we conquer it. Reading helps you to become your full, autonomous self.

We can also learn from Bloom that there is much value in paying attention to how you approach different types of writing. No one approach works all of the time. Short stories require the ability to pick up on clues as to what isn’t included. Poetry is more illuminating if memorized. The way we experience novels has a lot to do with who we are and our perception of its popularity. And plays teach us how much more there is going on beneath the surface of what we see.

One last time: why read?

“Because you will be haunted by great visions: of Ishmael, escaped alone to tell us; of Oedipa Mass, cradling the old derelict in her arms; of Invisible Man, preparing to come up again; like Jonah, out of the whale’s belly. All of them, on some of the higher frequencies, speak to and for you.”

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Why Life Can’t Be Simpler

Reading Time: 10 minutes

We’d all like life to be simpler. But we also don’t want to sacrifice our options and capabilities. Tesler’s law of the conservation of complexity, a rule from design, explains why we can’t have both. Here’s how the law can help us create better products and services by rethinking simplicity.

“Why can’t life be simple?”

We’ve all likely asked ourselves that at least once. After all, life is complicated. Every day, we face processes that seem almost infinitely recursive. Each step requires the completion of a different task to make it possible, which in itself requires another task. We confront tools requiring us to memorize reams of knowledge and develop additional skills just to use them. Endeavors that seem like they should be simple, like getting utilities connected in a new home or figuring out the controls for a fridge, end up having numerous perplexing steps.

When we wish for things to be simpler, we usually mean we want products and services to have fewer steps, fewer controls, fewer options, less to learn. But at the same time, we still want all of the same features and capabilities. These two categories of desires are often at odds with each other and distort how we understand the complex.

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Conceptual Models

In Living with Complexity, Donald A. Norman explains that complexity is all in the mind. Our perception of a product or service as simple or complex has its basis in the conceptual model we have of it. Norman writes that “A conceptual model is the underlying belief structure held by a person about how something works . . . Conceptual models are extremely important tools for organizing and understanding otherwise complex things.”

For example, on many computers, you can drag and drop a file into a folder. Both the file and the folder often have icons that represent their real-world namesakes. For the user, this process is simple; it provides a clear conceptual model. When people first started using graphical interfaces, real-world terms and icons made it easier to translate what they were doing. But the process only seems simple because of this effective conceptual model. It doesn’t represent what happens on the computer, where files and folders don’t exist. Computers store data wherever is convenient and may split files across multiple locations.

When we want something to be simpler, what we truly need is a better conceptual model of it. Once we know how to use them, complex tools end up making our lives simpler because they provide the precise functionality we want. A computer file is a great conceptual model because it hijacked something people already understood: physical files and folders. It would have been much harder for them to develop a whole new conceptual model reflecting how computers actually store files. What’s important to note is that giving users this simple conceptual model didn’t change how things work behind the scenes.

Removing functionality doesn’t make something simpler, because it removes options. Simple tools have a limited ability to simplify processes. Trying to do something complex with a simple tool is more complex than doing the same thing with a more complex tool.

A useful analogy here is the hand tools used by craftspeople, such as a silversmith’s planishing hammer (a tool used to shape and smooth the surface of metal). Norman highlights that these tools seem simple to the untrained eye. But using them requires great skill and practice. A craftsperson needs to know how to select them from the whole constellation of specialized tools they possess.

In itself, a planishing hammer might seem far, far simpler than, say, a digital photo editing program. Look again, Norman says. We have to compare the photo editing tool with the silversmith’s whole workbench. Both take a lot of time and practice to master. Both consist of many tools that are individually simple. Learning how and when to use them is the complex part.

Norman writes, “Whether something is complicated is in the mind of the beholder. ” Looking at a workbench of tools or a digital photo editing program, a novice sees complexity. A professional sees a range of different tools, each of which is simple to use. They know when to use each to make a process easier. Having fewer options would make their life more complex, not simpler, because they wouldn’t be able to break what they need to do down into individually simple steps. A professional’s experience-honed conceptual model helps them navigate a wide range of tools.

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The conservation of complexity

To do difficult things in the simplest way, we need a lot of options.

Complexity is necessary because it gives us the functionality we need. A useful framework for understanding this is Tesler’s law of the conservation of complexity, which states:

The total complexity of a system is a constant. If you make a user’s interaction with a system simpler, the complexity behind the scenes increases.

The law originates from Lawrence Tesler (1945–2020), a computer scientist specializing in human-computer interactions who worked at Xerox, Apple, Amazon, and Yahoo! Tesler was influential in the development of early graphical interfaces, and he was the co-creator of the copy-and-paste functionality.

Complexity is like energy. It cannot be created or destroyed, only moved somewhere else. When a product or service becomes simpler for users, engineers and designers have to work harder. Norman writes, “With technology, simplifications at the level of usage invariably result in added complexity of the underlying mechanism. ” For example, the files and folders conceptual model for computer interfaces doesn’t change how files are stored, but by putting in extra work to translate the process into something recognizable, designers make navigating them easier for users.

Whether something looks simple or is simple to use says little about its overall complexity. “What is simple on the surface can be incredibly complex inside: what is simple inside can result in an incredibly complex surface. So from whose point of view do we measure complexity? ”

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Out of control

Every piece of functionality requires a control—something that makes something happen. The more complex something is, the more controls it needs—whether they are visible to the user or not. Controls may be directly accessible to a user, as with the home button on an iPhone, or they may be behind the scenes, as with an automated thermostat.

From a user’s standpoint, the simplest products and services are those that are fully automated and do not require any intervention (unless something goes wrong.)

As long as you pay your bills, the water supply to your house is probably fully automated. When you turn on a tap, you don’t need to have requested there to be water in the pipes first. The companies that manage the water supply handle the complexity.

Or, if you stay in an expensive hotel, you might find your room is always as you want it, with your minifridge fully stocked with your favorites and any toiletries you forgot provided. The staff work behind the scenes to make this happen, without you needing to make requests.

On the other end of the spectrum, we have products and services that require users to control every last step.

A professional photographer is likely to use a camera that needs them to manually set every last setting, from white balance to shutter speed. This means the camera itself doesn’t need automation, but the user needs to operate controls for everything, giving them full control over the results. An amateur photographer might use a camera that automatically chooses these settings so all they need to do is point and shoot. In this case, the complexity transfers to the camera’s inner workings.

In the restaurants inside IKEA stores, customers typically perform tasks such as filling up drinks and clearing away dishes themselves. This means less complexity for staff and much lower prices compared to restaurants where staff do these things.

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Lessons from the conservation of complexity

The first lesson from Tesler’s law of the conservation of complexity is that how simple something looks is not a reflection of how simple it is to use. Removing controls can mean users need to learn complex sequences to use the same features—similar to how languages with fewer sounds have longer words. One way to conceptualize the movement of complexity is through the notion of trade-offs. If complexity is constant, then there are trade-offs depending on where that complexity is moved.

A very basic example of complexity trade-offs can be found in the history of arithmetic. For centuries, many counting systems all over the world employed tools using stones or beads like a tabula (the Romans) or soroban (the Japanese) to facilitate adding and subtracting numbers. They were easy to use, but not easily portable. Then the Hindu-Arabic system came along (the one we use today) and by virtue of employing columns, and thus not requiring any moving parts, offered a much more portable counting system. However, the portability came with a cost.

Paul Lockhart explains in Arithmetic, “With the Hindu-Arabic system the writing and calculating are inextricably linked. Instead of moving stones or sliding beads, our manipulations become transmutations of the symbols themselves. That means we need to know things. We need to know that one more than 2 is 3, for instance. In other words, the price we pay [for portability] is massive amounts of memorization.” Thus, there is a trade-off. The simpler arithmetic system requires more complexity in terms of the memorization required of the users. We all went through the difficult process of learning mathematical symbols early in life. Although they might seem simple to us now, that’s just because we’re so accustomed to them.

Although perceived simplicity may have greater appeal at first, users are soon frustrated if it means greater operational complexity. Norman writes:

Perceived simplicity is not at all the same as simplicity of usage: operational simplicity. Perceived simplicity decreases with the number of visible controls and displays. Increase the number of visible alternatives and the perceived simplicity drops. The problem is that operational simplicity can be drastically improved by adding more controls and displays. The very things that make something easier to learn and to use can also make it be perceived as more difficult.

Even if it receives a negative reaction before usage, operational simplicity is the more important goal. For example, in a company, having a clearly stated directly responsible person for each project might seem more complex than letting a project be a team effort that falls to whoever is best suited to each part. But in practice, this adds complexity when someone tries to move forward with it or needs to know who should hear feedback about problems.

A second lesson is that things don’t always need to be incredibly simple for users. People have an intuitive sense that complexity has to go somewhere. When using a product or service is too simple, users can feel suspicious or like they’ve been robbed of control. They know that a lot more is going on behind the scenes, they just don’t know what it is. Sometimes we need to preserve a minimum level of complexity so that users feel like an actual participant. According to legend, cake mixes require the addition of a fresh egg because early users found that dried ones felt a bit too lazy and low effort.

An example of desirable minimum complexity is help with homework. For many parents, helping their children with their homework often feels like unnecessary complexity. It is usually subjects and facts they haven’t thought about in years, and they find themselves having to relearn them in order to help their kids. It would be far simpler if the teachers could cover everything in class to a degree that each child needed no additional practice. However, the complexity created by involving parents in the homework process helps make parents more aware of what their children are learning. In addition, they often get insight into areas of both struggle and interest, can identify ways to better connect with their children, and learn where they may want to teach them some broader life skills.

When we seek to make things simpler for other people, we should recognize that there be a point of diminishing negative returns wherein further simplification leads to a worse experience. Simplicity is not an end in itself—other things like speed, usability, and time-saving are. We shouldn’t simplify things from the user standpoint for the sake of it.

If changes don’t make something better for users, we’re just creating unnecessary behind-the-scenes complexity. People want to feel in control, especially when it comes to something important. We want to learn a bit about what’s happening, and an overly simple process teaches us nothing.

A third lesson is that products and services are only as good as what happens when they break. Handling a problem with something that has lots of controls on the user side may be easier for the user. They’re used to being involved in it. If something has been fully automated up until the point where it breaks, users don’t know how to react. The change is jarring, and they may freeze or overreact. Seeing as fully automated things fade into the background, this may be their most salient and memorable interaction with a product or service. If handling a problem is difficult for the user—for example, if there’s a lack of rapid support or instructions available or it’s hard to ascertain what went wrong in the first place—they may come away with a negative overall impression, even if everything worked fine for years beforehand.

A big challenge in the development of self-driving cars is that a driver needs to be able to take over if the car encounters a problem. But if someone hasn’t had to operate the car manually for a while, they may panic or forget what to do. So it’s a good idea to limit how long the car drives itself for. The same is purportedly true for airplane pilots. If the plane does too much of the work, the pilot won’t cope well in an emergency.

A fourth lesson is the importance of thinking about how the level of control you give your customers or users influences your workload. For a graphic designer, asking a client to detail exactly how they want their logo to look makes their work simpler. But it might be hard work for the client, who might not know what they want or may make poor choices. A more experienced designer might ask a client for much less information and instead put the effort into understanding their overall brand and deducing their needs from subtle clues, then figuring out the details themselves. The more autonomy a manager gives their team, the lower their workload, and vice versa.

If we accept that complexity is a constant, we need to always be mindful of who is bearing the burden of that complexity.

 

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Being Smart is Not Enough

Reading Time: 3 minutes

When hiring a team, we tend to favor the geniuses who hatch innovative ideas, but overlook the butterflies, the crucial ones who share and implement them. Here’s why it’s important to be both smart AND social.

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In business, it’s never enough to have a great idea. For any innovation to be successful, it has to be shared, promoted, and bought into by everyone in the organization. Yet often we focus on the importance of those great ideas and seem to forget about the work that is required to spread them around.

Whenever we are building a team, we tend to look for smarts. We are attracted to those with lots of letters after their names or fancy awards on their resumes. We assume that if we hire the smartest people we can find, they will come up with new, better ways of doing things that save us time and money.

Conversely, we often look down on predominantly social people. They seem to spend too much time gossiping and not enough time working. We assume they’ll be too busy engaging on social media or away from their desks too often to focus on their duties, and thus we avoid hiring them.

Although we aren’t going to tell you to swear off smarts altogether, we are here to suggest that maybe it’s time to reconsider the role that social people play in cultural growth and the diffusion of innovation.

In his book, The Secret of Our Success: How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter, Joseph Henrich explores the role of culture in human evolution. One point he makes is that it’s not enough for a species to be smart. What counts far more is having the cultural infrastructure to share, teach, and learn.

Consider two very large prehuman populations, the Geniuses and the Butterflies. Suppose the Geniuses will devise an invention once in 10 lifetimes. The Butterflies are much dumber, only devising the same invention once in 1000 lifetimes. So, this means that the Geniuses are 100 times smarter than the Butterflies. However, the Geniuses are not very social and have only 1 friend they can learn from. The Butterflies have 10 friends, making them 10 times more social.

Now, everyone in both populations tries to obtain an invention, both by figuring it out for themselves and by learning from friends. Suppose learning from friends is difficult: if a friend has it, a learner only learns it half the time. After everyone has done their own individual learning and tried to learn from their friends, do you think the innovation will be more common among the Geniuses or the Butterflies?

Well, among the Geniuses a bit fewer than 1 out of 5 individuals (18%) will end up with the invention. Half of those Geniuses will have figured it out all by themselves. Meanwhile, 99.9% of Butterflies will have the innovation, but only 0.1% will have figured it out by themselves.

Wow.

What if we take this thinking and apply to the workplace? Of course you want to have smart people. But you don’t want an organization full of Geniuses. They might come up with a lot, but without being able to learn from each other easily, many of their ideas won’t have any uptake in the organization. Instead, you’d want to pair Geniuses with Butterflies—socially attuned people who are primed to adopt the successful behaviors of those around them.

If you think you don’t need Butterflies because you can just put Genius innovations into policy and procedure, you’re missing the point. Sure, some brilliant ideas are concrete, finite, and visible. Those are the ones you can identify and implement across the organization from the top down. But some of the best ideas happen on the fly in isolated, one-off situations as responses to small changes in the environment. Perhaps there’s a minor meeting with a client, and the Genius figures out a new way of describing your product that really resonates. The Genius though, is not a teacher. It worked for them and they keep repeating the behavior, but it doesn’t occur to them to teach someone else. And they don’t pick up on other tactics to further refine their innovation.

But the Butterfly who went to the meeting with the Genius? They pick up on the successful new product description right away. They emulate it in all meetings from then on. They talk about it with their friends, most of whom are also Butterflies. Within two weeks, the new description has taken off because of the propensity for cultural learning embedded in the social Butterflies.

The lesson here is to hire both types of people. Know that it’s the Geniuses who innovate, but it’s the Butterflies who spread that innovation around. Both components are required for successfully implementing new, brilliant ideas.

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The Spiral of Silence

Reading Time: 5 minutes

Our desire to fit in with others means we don’t always say what we think. We only express opinions that seem safe. Here’s how the spiral of silence works and how we can discover what people really think.

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Be honest: How often do you feel as if you’re really able to express your true opinions without fearing judgment? How often do you bite your tongue because you know you hold an unpopular view? How often do you avoid voicing any opinion at all for fear of having misjudged the situation?

Even in societies with robust free speech protections, most people don’t often say what they think. Instead they take pains to weigh up the situation and adjust their views accordingly. This comes down to the “spiral of silence,” a human communication theory developed by German researcher Elisabeth Noelle-Neumann in the 1960s and ’70s. The theory explains how societies form collective opinions and how we make decisions surrounding loaded topics.

Let’s take a look at how the spiral of silence works and how understanding it can give us a more realistic picture of the world.

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How the spiral of silence works

According to Noelle-Neumann’s theory, our willingness to express an opinion is a direct result of how popular or unpopular we perceive it to be. If we think an opinion is unpopular, we will avoid expressing it. If we think it is popular, we will make a point of showing we think the same as others.

Controversy is also a factor—we may be willing to express an unpopular uncontroversial opinion but not an unpopular controversial one. We perform a complex dance whenever we share views on anything morally loaded.

Our perception of how “safe” it is to voice a particular view comes from the clues we pick up, consciously or not, about what everyone else believes. We make an internal calculation based on signs like what the mainstream media reports, what we overhear coworkers discussing on coffee breaks, what our high school friends post on Facebook, or prior responses to things we’ve said.

We also weigh up the particular context, based on factors like how anonymous we feel or whether our statements might be recorded.

As social animals, we have good reason to be aware of whether voicing an opinion might be a bad idea. Cohesive groups tend to have similar views. Anyone who expresses an unpopular opinion risks social exclusion or even ostracism within a particular context or in general. This may be because there are concrete consequences, such as losing a job or even legal penalties. Or there may be less official social consequences, like people being less friendly or willing to associate with you. Those with unpopular views may suppress them to avoid social isolation.

Avoiding social isolation is an important instinct. From an evolutionary biology perspective, remaining part of a group is important for survival, hence the need to at least appear to share the same views as anyone else. The only time someone will feel safe to voice a divergent opinion is if they think the group will share it or be accepting of divergence, or if they view the consequences of rejection as low. But biology doesn’t just dictate how individuals behave—it ends up shaping communities. It’s almost impossible for us to step outside of that need for acceptance.

A feedback loop pushes minority opinions towards less and less visibility—hence why Noelle-Neumann used the word “spiral.” Each time someone voices a majority opinion, they reinforce the sense that it is safe to do so. Each time someone receives a negative response for voicing a minority opinion, it signals to anyone sharing their view to avoid expressing it.

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An example of the spiral of silence

A 2014 Pew Research survey of 1,801 American adults examined the prevalence of the spiral of silence on social media. Researchers asked people about their opinions on one public issue: Edward Snowden’s 2013 revelations of US government surveillance of citizens’ phones and emails. They selected this issue because, while controversial, prior surveys suggested a roughly even split in public opinion surrounding whether the leaks were justified and whether such surveillance was reasonable.

Asking respondents about their willingness to share their opinions in different contexts highlighted how the spiral of silence plays out. 86% of respondents were willing to discuss the issue in person, but only about half as many were willing to post about it on social media. Of the 14% who would not consider discussing the Snowden leaks in person, almost none (0.3%) were willing to turn to social media instead.

Both in person and online, respondents reported far greater willingness to share their views with people they knew agreed with them—three times as likely in the workplace and twice as likely in a Facebook discussion.

***

The implications of the spiral of silence

The end result of the spiral of silence is a point where no one publicly voices a minority opinion, regardless of how many people believe it. The first implication of this is that the picture we have of what most people believe is not always accurate. Many people nurse opinions they would never articulate to their friends, coworkers, families, or social media followings.

A second implication is that the possibility of discord makes us less likely to voice an opinion at all, assuming we are not trying to drum up conflict. In the aforementioned Pew survey, people were more comfortable discussing a controversial story in person than online. An opinion voiced online has a much larger potential audience than one voiced face to face, and it’s harder to know exactly who will see it. Both of these factors increase the risk of someone disagreeing.

If we want to gauge what people think about something, we need to remove the possibility of negative consequences. For example, imagine a manager who often sets overly tight deadlines, causing immense stress to their team. Everyone knows this is a problem and discusses it among themselves, recognizing that more realistic deadlines would be motivating, and unrealistic ones are just demoralizing. However, no one wants to say anything because they’ve heard the manager say that people who can’t handle pressure don’t belong in that job. If the manager asks for feedback about their leadership style, they’re not going to hear what they need to hear if they know who it comes from.

A third implication is that what seems like a sudden change in mainstream opinions can in fact be the result of a shift in what is acceptable to voice, not in what people actually think. A prominent public figure getting away with saying something controversial may make others feel safe to do the same. A change in legislation may make people comfortable saying what they already thought.

For instance, if recreational marijuana use is legalized where someone lives, they might freely remark to a coworker that they consume it and consider it harmless. Even if that was true before the legislation change, saying so would have been too fraught, so they might have lied or avoided the topic. The result is that mainstream opinions can appear to change a great deal in a short time.

A fourth implication is that highly vocal holders of a minority opinion can end up having a disproportionate influence on public discourse. This is especially true if that minority is within a group that already has a lot of power.

While this was less the case during Noelle-Neumann’s time, the internet makes it possible for a vocal minority to make their opinions seem far more prevalent than they actually are—and therefore more acceptable. Indeed, the most extreme views on any spectrum can end up seeming most normal online because people with a moderate take have less of an incentive to make themselves heard.

In anonymous environments, the spiral of silence can end up reversing itself, making the most fringe views the loudest.

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