Tag: Donald A. Norman

Why Life Can’t Be Simpler

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.

 

Complexity Bias: Why We Prefer Complicated to Simple

Complexity bias is a logical fallacy that leads us to give undue credence to complex concepts.

Faced with two competing hypotheses, we are likely to choose the most complex one. That’s usually the option with the most assumptions and regressions. As a result, when we need to solve a problem, we may ignore simple solutions — thinking “that will never work” — and instead favor complex ones.

To understand complexity bias, we need first to establish the meaning of three key terms associated with it: complexity, simplicity, and chaos.

The Cambridge Dictionary defines complexity as “the state of having many parts and being difficult to understand or find an answer to.” The definition of simplicity is the inverse: “something [that] is easy to understand or do.” Chaos is defined as “a state of total confusion with no order.”

“Life is really simple, but we insist on making it complicated.”

— Confucius

Complex systems contain individual parts that combine to form a collective that often can’t be predicted from its components. Consider humans. We are complex systems. We’re made of about 100 trillion cells and yet we are so much more than the aggregation of our cells. You’d never predict what we’re like or who we are from looking at our cells.

Complexity bias is our tendency to look at something that is easy to understand, or look at it when we are in a state of confusion, and view it as having many parts that are difficult to understand.

We often find it easier to face a complex problem than a simple one.

A person who feels tired all the time might insist that their doctor check their iron levels while ignoring the fact that they are unambiguously sleep deprived. Someone experiencing financial difficulties may stress over the technicalities of their telephone bill while ignoring the large sums of money they spend on cocktails.

Marketers make frequent use of complexity bias.

They do this by incorporating confusing language or insignificant details into product packaging or sales copy. Most people who buy “ammonia-free” hair dye, or a face cream which “contains peptides,” don’t fully understand the claims. Terms like these often mean very little, but we see them and imagine that they signify a product that’s superior to alternatives.

How many of you know what probiotics really are and how they interact with gut flora?

Meanwhile, we may also see complexity where only chaos exists. This tendency manifests in many forms, such as conspiracy theories, superstition, folklore, and logical fallacies. The distinction between complexity and chaos is not a semantic one. When we imagine that something chaotic is in fact complex, we are seeing it as having an order and more predictability than is warranted. In fact, there is no real order, and prediction is incredibly difficult at best.

Complexity bias is interesting because the majority of cognitive biases occur in order to save mental energy. For example, confirmation bias enables us to avoid the effort associated with updating our beliefs. We stick to our existing opinions and ignore information that contradicts them. Availability bias is a means of avoiding the effort of considering everything we know about a topic. It may seem like the opposite is true, but complexity bias is, in fact, another cognitive shortcut. By opting for impenetrable solutions, we sidestep the need to understand. Of the fight-or-flight responses, complexity bias is the flight response. It is a means of turning away from a problem or concept and labeling it as too confusing. If you think something is harder than it is, you surrender your responsibility to understand it.

“Most geniuses—especially those who lead others—prosper not by deconstructing intricate complexities but by exploiting unrecognized simplicities.”

— Andy Benoit

Faced with too much information on a particular topic or task, we see it as more complex than it is. Often, understanding the fundamentals will get us most of the way there. Software developers often find that 90% of the code for a project takes about half the allocated time. The remaining 10% takes the other half. Writing — and any other sort of creative work — is much the same. When we succumb to complexity bias, we are focusing too hard on the tricky 10% and ignoring the easy 90%.

Research has revealed our inherent bias towards complexity.

In a 1989 paper entitled “Sensible reasoning in two tasks: Rule discovery and hypothesis evaluation,” Hilary F. Farris and Russell Revlin evaluated the topic. In one study, participants were asked to establish an arithmetic rule. They received a set of three numbers (such as 2, 4, 6) and tried to generate a hypothesis by asking the experimenter if other number sequences conformed to the rule. Farris and Revlin wrote, “This task is analogous to one faced by scientists, with the seed triple functioning as an initiating observation, and the act of generating the triple is equivalent to performing an experiment.”

The actual rule was simple: list any three ascending numbers.

The participants could have said anything from “1, 2, 3” to “3, 7, 99” and been correct. It should have been easy for the participants to guess this, but most of them didn’t. Instead, they came up with complex rules for the sequences. (Also see Falsification of Your Best Loved Ideas.)

A paper by Helena Matute looked at how intermittent reinforcement leads people to see complexity in chaos. Three groups of participants were placed in rooms and told that a loud noise would play from time to time. The volume, length, and pattern of the sound were identical for each group. Group 1 (Control) was told to sit and listen to the noises. Group 2 (Escape) was told that there was a specific action they could take to stop the noises. Group 3 (Yoked) was told the same as Group 2, but in their case, there was actually nothing they could do.

Matute wrote:

Yoked participants received the same pattern and duration of tones that had been produced by their counterparts in the Escape group. The amount of noise received by Yoked and Control subjects depends only on the ability of the Escape subjects to terminate the tones. The critical factor is that Yoked subjects do not have control over reinforcement (noise termination) whereas Escape subjects do, and Control subjects are presumably not affected by this variable.

The result? Not one member of the Yoked group realized that they had no control over the sounds. Many members came to repeat particular patterns of “superstitious” behavior. Indeed, the Yoked and Escape groups had very similar perceptions of task controllability. Faced with randomness, the participants saw complexity.

Does that mean the participants were stupid? Not at all. We all exhibit the same superstitious behavior when we believe we can influence chaotic or simple systems.

Funnily enough, animal studies have revealed much the same. In particular, consider B.F. Skinner’s well-known research on the effects of random rewards on pigeons. Skinner placed hungry pigeons in cages equipped with a random-food-delivery mechanism. Over time, the pigeons came to believe that their behavior affected the food delivery. Skinner described this as a form of superstition. One bird spun in counterclockwise circles. Another butted its head against a corner of the cage. Other birds swung or bobbed their heads in specific ways. Although there is some debate as to whether “superstition” is an appropriate term to apply to birds, Skinner’s research shed light on the human tendency to see things as being more complex than they actually are.

Skinner wrote (in “‘Superstition’ in the Pigeon,” Journal of Experimental Psychology, 38):

The bird behaves as if there were a causal relation between its behavior and the presentation of food, although such a relation is lacking. There are many analogies in human behavior. Rituals for changing one’s fortune at cards are good examples. A few accidental connections between a ritual and favorable consequences suffice to set up and maintain the behavior in spite of many unreinforced instances. The bowler who has released a ball down the alley but continues to behave as if he were controlling it by twisting and turning his arm and shoulder is another case in point. These behaviors have, of course, no real effect upon one’s luck or upon a ball half way down an alley, just as in the present case the food would appear as often if the pigeon did nothing—or, more strictly speaking, did something else.

The world around us is a chaotic, entropic place. But it is rare for us to see it that way.

In Living with Complexity, Donald A. Norman offers a perspective on why we need complexity:

We seek rich, satisfying lives, and richness goes along with complexity. Our favorite songs, stories, games, and books are rich, satisfying, and complex. We need complexity even while we crave simplicity… Some complexity is desirable. When things are too simple, they are also viewed as dull and uneventful. Psychologists have demonstrated that people prefer a middle level of complexity: too simple and we are bored, too complex and we are confused. Moreover, the ideal level of complexity is a moving target, because the more expert we become at any subject, the more complexity we prefer. This holds true whether the subject is music or art, detective stories or historical novels, hobbies or movies.

As an example, Norman asks readers to contemplate the complexity we attach to tea and coffee. Most people in most cultures drink tea or coffee each day. Both are simple beverages, made from water and coffee beans or tea leaves. Yet we choose to attach complex rituals to them. Even those of us who would not consider ourselves to be connoisseurs have preferences. Offer to make coffee for a room full of people, and we can be sure that each person will want it made in a different way.

Coffee and tea start off as simple beans or leaves, which must be dried or roasted, ground and infused with water to produce the end result. In principle, it should be easy to make a cup of coffee or tea. Simply let the ground beans or tea leaves [steep] in hot water for a while, then separate the grounds and tea leaves from the brew and drink. But to the coffee or tea connoisseur, the quest for the perfect taste is long-standing. What beans? What tea leaves? What temperature water and for how long? And what is the proper ratio of water to leaves or coffee?

The quest for the perfect coffee or tea maker has been around as long as the drinks themselves. Tea ceremonies are particularly complex, sometimes requiring years of study to master the intricacies. For both tea and coffee, there has been a continuing battle between those who seek convenience and those who seek perfection.

Complexity, in this way, can enhance our enjoyment of a cup of tea or coffee. It’s one thing to throw some instant coffee in hot water. It’s different to select the perfect beans, grind them ourselves, calculate how much water is required, and use a fancy device. The question of whether this ritual makes the coffee taste better or not is irrelevant. The point is the elaborate surrounding ritual. Once again, we see complexity as superior.

“Simplicity is a great virtue but it requires hard work to achieve it and education to appreciate it. And to make matters worse: complexity sells better.”

— Edsger W. Dijkstra

The Problem with Complexity

Imagine a person who sits down one day and plans an elaborate morning routine. Motivated by the routines of famous writers they have read about, they lay out their ideal morning. They decide they will wake up at 5 a.m., meditate for 15 minutes, drink a liter of lemon water while writing in a journal, read 50 pages, and then prepare coffee before planning the rest of their day.

The next day, they launch into this complex routine. They try to keep at it for a while. Maybe they succeed at first, but entropy soon sets in and the routine gets derailed. Sometimes they wake up late and do not have time to read. Their perceived ideal routine has many different moving parts. Their actual behavior ends up being different each day, depending on random factors.

Now imagine that this person is actually a famous writer. A film crew asks to follow them around on a “typical day.” On the day of filming, they get up at 7 a.m., write some ideas, make coffee, cook eggs, read a few news articles, and so on. This is not really a routine; it is just a chaotic morning based on reactive behavior. When the film is posted online, people look at the morning and imagine they are seeing a well-planned routine rather than the randomness of life.

This hypothetical scenario illustrates the issue with complexity: it is unsustainable without effort.

The more individual constituent parts a system has, the greater the chance of its breaking down. Charlie Munger once said that “Where you have complexity, by nature you can have fraud and mistakes.” Any complex system — be it a morning routine, a business, or a military campaign — is difficult to manage. Addressing one of the constituent parts inevitably affects another (see the Butterfly Effect). Unintended and unexpected consequences are likely to occur.

As Daniel Kahneman and Amos Tversky wrote in 1974 (in Judgment Under Uncertainty: Heuristics and Biases): “A complex system, such as a nuclear reactor or the human body, will malfunction if any of its essential components fails. Even when the likelihood of failure in each component is slight, the probability of an overall failure can be high if many components are involved.”

This is why complexity is less common than we think. It is unsustainable without constant maintenance, self-organization, or adaptation. Chaos tends to disguise itself as complexity.

“Human beings are pattern-seeking animals. It’s part of our DNA. That’s why conspiracy theories and gods are so popular: we always look for the wider, bigger explanations for things.”

— Adrian McKinty, The Cold Cold Ground

Complexity Bias and Conspiracy Theories

A musician walks barefoot across a zebra-crossing on an album cover. People decide he died in a car crash and was replaced by a lookalike. A politician’s eyes look a bit odd in a blurry photograph. People conclude that he is a blood-sucking reptilian alien taking on a human form. A photograph shows an indistinct shape beneath the water of a Scottish lake. The area floods with tourists hoping to glimpse a surviving prehistoric creature. A new technology overwhelms people. So, they deduce that it is the product of a government mind-control program.

Conspiracy theories are the ultimate symptom of our desire to find complexity in the world. We don’t want to acknowledge that the world is entropic. Disasters happen and chaos is our natural state. The idea that hidden forces animate our lives is an appealing one. It seems rational. But as we know, we are all much less rational and logical than we think. Studies have shown that a high percentage of people believe in some sort of conspiracy. It’s not a fringe concept. According to research by Joseph E. Uscinski and Joseph M. Parent, about one-third of Americans believe the notion that Barack Obama’s birth certificate is fake. Similar numbers are convinced that 9/11 was an inside job orchestrated by George Bush. Beliefs such as these are present in all types of people, regardless of class, age, gender, race, socioeconomic status, occupation, or education level.

Conspiracy theories are invariably far more complex than reality. Although education does reduce the chances of someone’s believing in conspiracy theories, one in five Americans with postgraduate degrees still hold conspiratorial beliefs.

Uscinski and Parent found that, just as uncertainty led Skinner’s pigeons to see complexity where only randomness existed, a sense of losing control over the world around us increases the likelihood of our believing in conspiracy theories. Faced with natural disasters and political or economic instability, we are more likely to concoct elaborate explanations. In the face of horrific but chaotic events such as Hurricane Katrina, or the recent Grenfell Tower fire, many people decide that secret institutions are to blame.

Take the example of the “Paul McCartney is dead” conspiracy theory. Since the 1960s, a substantial number of people have believed that McCartney died in a car crash and was replaced by a lookalike, usually said to be a Scottish man named William Campbell. Of course, conspiracy theorists declare, The Beatles wanted their most loyal fans to know this, so they hid clues in songs and on album covers.

The beliefs surrounding the Abbey Road album are particularly illustrative of the desire to spot complexity in randomness and chaos. A police car is parked in the background — an homage to the officers who helped cover up the crash. A car’s license plate reads “LMW 28IF” — naturally, a reference to McCartney being 28 if he had lived (although he was 27) and to Linda McCartney (whom he had not met yet). Matters were further complicated once The Beatles heard about the theory and began to intentionally plant “clues” in their music. The song “I’m So Tired” does in fact feature backwards mumbling about McCartney’s supposed death. The 1960s were certainly a turbulent time, so is it any wonder that scores of people pored over album art or played records backwards, looking for evidence of a complex hidden conspiracy?

As Henry Louis Gates Jr. wrote, “Conspiracy theories are an irresistible labor-saving device in the face of complexity.”

Complexity Bias and Language

We have all, at some point, had a conversation with someone who speaks like philosopher Theodor Adorno wrote: using incessant jargon and technical terms even when simpler synonyms exist and would be perfectly appropriate. We have all heard people say things which we do not understand, but which we do not question for fear of sounding stupid.

Jargon is an example of how complexity bias affects our communication and language usage. When we use jargon, especially out of context, we are putting up unnecessary semantic barriers that reduce the chances of someone’s challenging or refuting us.

In an article for The Guardian, James Gingell describes his work translating scientific jargon into plain, understandable English:

It’s quite simple really. The first step is getting rid of the technical language. Whenever I start work on refining a rough-hewn chunk of raw science into something more pleasant I use David Dobbs’ (rather violent) aphorism as a guiding principle: “Hunt down jargon like a mercenary possessed, and kill it.” I eviscerate acronyms and euthanise decrepit Latin and Greek. I expunge the esoteric. I trim and clip and pare and hack and burn until only the barest, most easily understood elements remain.

[…]

Jargon…can be useful for people as a shortcut to communicating complex concepts. But it’s intrinsically limited: it only works when all parties involved know the code. That may be an obvious point but it’s worth emphasising — to communicate an idea to a broad, non-specialist audience, it doesn’t matter how good you are at embroidering your prose with evocative imagery and clever analogies, the jargon simply must go.”

Gingell writes that even the most intelligent scientists struggle to differentiate between thinking (and speaking and writing) like a scientist, and thinking like a person with minimal scientific knowledge.

Unnecessarily complex language is not just annoying. It’s outright harmful. The use of jargon in areas such as politics and economics does real harm. People without the requisite knowledge to understand it feel alienated and removed from important conversations. It leads people to believe that they are not intelligent enough to understand politics, or not educated enough to comprehend economics. When a politician talks of fiscal charters or rolling four-quarter growth measurements in a public statement, they are sending a crystal clear message to large numbers of people whose lives will be shaped by their decisions: this is not about you.

Complexity bias is a serious issue in politics. For those in the public eye, complex language can be a means of minimizing the criticism of their actions. After all, it is hard to dispute something you don’t really understand. Gingell considers jargon to be a threat to democracy:

If we can’t fully comprehend the decisions that are made for us and about us by the government then how we can we possibly revolt or react in an effective way? Yes, we have a responsibility to educate ourselves more on the big issues, but I also think it’s important that politicians and journalists meet us halfway.

[…]

Economics and economic decisions are more important than ever now, too. So we should implore our journalists and politicians to write and speak to us plainly. Our democracy depends on it.

In his essay “Politics and the English Language,” George Orwell wrote:

In our time, political speech and writing are largely the defence of the indefensible. … Thus, political language has to consist largely of euphemism, question-begging and sheer cloudy vagueness. Defenceless villages are bombarded from the air, the inhabitants driven out into the countryside, the cattle machine-gunned, the huts set on fire with incendiary bullets: this is called pacification. Millions of peasants are robbed of their farms and sent trudging along the roads with no more than they can carry: this is called transfer of population or rectification of frontiers. People are imprisoned for years without trial, or shot in the back of the neck or sent to die of scurvy in Arctic lumber camps: this is called elimination of unreliable elements.

An example of the problems with jargon is the Sokal affair. In 1996, Alan Sokal (a physics professor) submitted a fabricated scientific paper entitled “Transgressing the Boundaries: Towards a Transformative Hermeneutics of Quantum Gravity.” The paper had absolutely no relation to reality and argued that quantum gravity is a social and linguistic construct. Even so, the paper was published in a respected journal. Sokal’s paper consisted of convoluted, essentially meaningless claims, such as this paragraph:

Secondly, the postmodern sciences deconstruct and transcend the Cartesian metaphysical distinctions between humankind and Nature, observer and observed, Subject and Object. Already quantum mechanics, earlier in this century, shattered the ingenious Newtonian faith in an objective, pre-linguistic world of material objects “out there”; no longer could we ask, as Heisenberg put it, whether “particles exist in space and time objectively.”

(If you’re wondering why no one called him out, or more specifically why we have a bias to not call BS out, check out pluralistic ignorance).

Jargon does have its place. In specific contexts, it is absolutely vital. But in everyday communication, its use is a sign that we wish to appear complex and therefore more intelligent. Great thinkers throughout the ages have stressed the crucial importance of using simple language to convey complex ideas. Many of the ancient thinkers whose work we still reference today — people like Plato, Marcus Aurelius, Seneca, and Buddha — were known for their straightforward communication and their ability to convey great wisdom in a few words.

“Any intelligent fool can make things bigger, more complex, and more violent. It takes a touch of genius — and a lot of courage — to move in the opposite direction.”

— Ernst F. Schumacher

How Can We Overcome Complexity Bias?

The most effective tool we have for overcoming complexity bias is Occam’s razor. Also known as the principle of parsimony, this is a problem-solving principle used to eliminate improbable options in a given situation. Occam’s razor suggests that the simplest solution or explanation is usually the correct one. When we don’t have enough empirical evidence to disprove a hypothesis, we should avoid making unfounded assumptions or adding unnecessary complexity so we can make quick decisions or establish truths.

An important point to note is that Occam’s razor does not state that the simplest hypothesis is the correct one, but states rather that it is the best option before the establishment of empirical evidence. It is also useful in situations where empirical data is difficult or impossible to collect. While complexity bias leads us towards intricate explanations and concepts, Occam’s razor can help us to trim away assumptions and look for foundational concepts.

Returning to Skinner’s pigeons, had they known of Occam’s razor, they would have realized that there were two main possibilities:

  • Their behavior affects the food delivery.

Or:

  • Their behavior is irrelevant because the food delivery is random or on a timed schedule.

Using Occam’s razor, the head-bobbing, circles-turning pigeons would have realized that the first hypothesis involves numerous assumptions, including:

  • There is a particular behavior they must enact to receive food.
  • The delivery mechanism can somehow sense when they enact this behavior.
  • The required behavior is different from behaviors that would normally give them access to food.
  • The delivery mechanism is consistent.

And so on. Occam’s razor would dictate that because the second hypothesis is the simplest, involving the fewest assumptions, it is most likely the correct one.

So many geniuses, are really good at eliminating unnecessary complexity. Einstein, for instance, was a master at sifting the essential from the non-essential. Steve Jobs was the same.

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