Tag: Simplicity

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.

 

How to Use Occam’s Razor Without Getting Cut

Occam’s razor is one of the most useful, (yet misunderstood,) models in your mental toolbox to solve problems more quickly and efficiently. Here’s how to use it.

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Occam’s razor (also known as the “law of parsimony”) is a problem-solving principle which serves as a useful mental model. A philosophical razor is a tool used to eliminate improbable options in a given situation. Occam’s is the best-known example.

Occam’s razor can be summarized as follows:

Among competing hypotheses, the one with the fewest assumptions should be selected.

The Basics

In simpler language, Occam’s razor states that the simplest explanation is preferable to one that is more complex. Simple theories are easier to verify. Simple solutions are easier to execute.

In other words, we should avoid looking for excessively complex solutions to a problem, and focus on what works given the circumstances. Occam’s razor can be used in a wide range of situations, as a means of making rapid decisions and establishing truths without empirical evidence. It works best as a mental model for making initial conclusions before the full scope of information can be obtained.

Science and math offer interesting lessons that demonstrate the value of simplicity. For example, the principle of minimum energy supports Occam’s razor. This facet of the second law of thermodynamics states that wherever possible, the use of energy is minimized. Physicists use Occam’s razor in the knowledge that they can rely on everything to use the minimum energy necessary to function. A ball at the top of a hill will roll down in order to be at the point of minimum potential energy. The same principle is present in biology. If a person repeats the same action on a regular basis in response to the same cue and reward, it will become a habit as the corresponding neural pathway is formed. From then on, their brain will use less energy to complete the same action.

The History of Occam’s Razor

The concept of Occam’s razor is credited to William of Ockham, a 14th-century friar, philosopher, and theologian. While he did not coin the term, his characteristic way of making deductions inspired other writers to develop the heuristic. Indeed, the concept of Occam’s razor is an ancient one. Aristotle produced the oldest known statement of the concept, saying, “We may assume the superiority, other things being equal, of the demonstration which derives from fewer postulates or hypotheses.”

Robert Grosseteste expanded on Aristotle’s writing in the 1200s, declaring

That is better and more valuable which requires fewer, other circumstances being equal…. For if one thing were demonstrated from many and another thing from fewer equally known premises, clearly that is better which is from fewer because it makes us know quickly, just as a universal demonstration is better than particular because it produces knowledge from fewer premises. Similarly, in natural science, in moral science, and in metaphysics the best is that which needs no premises and the better that which needs the fewer, other circumstances being equal.

Nowadays, Occam’s razor is an established mental model which can form a useful part of a latticework of knowledge.

Mental Model Occam's Razor

Examples of the Use of Occam’s Razor

The Development of Scientific Theories

Occam’s razor is frequently used by scientists, in particular for theoretical matters. The simpler a hypothesis is, the more easily it can be proven or falsified. A complex explanation for a phenomenon involves many factors which can be difficult to test or lead to issues with the repeatability of an experiment. As a consequence, the simplest solution which is consistent with the existing data is preferred. However, it is common for new data to allow hypotheses to become more complex over time. Scientists choose to opt for the simplest solution as the current data permits, while remaining open to the possibility of future research allowing for greater complexity.

The version used by scientists can best be summarized as:

When you have two competing theories that make exactly the same predictions, the simpler one is better.

The use of Occam’s razor in science is also a matter of practicality. Obtaining funding for simpler hypotheses tends to be easier, as they are often cheaper to prove.

Albert Einstein referred to Occam’s razor when developing his theory of special relativity. He formulated his own version: “It can scarcely be denied that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience.” Or, “Everything should be made as simple as possible, but not simpler.”

The physicist Stephen Hawking advocates for Occam’s razor in A Brief History of Time:

We could still imagine that there is a set of laws that determines events completely for some supernatural being, who could observe the present state of the universe without disturbing it. However, such models of the universe are not of much interest to us mortals. It seems better to employ the principle known as Occam’s razor and cut out all the features of the theory that cannot be observed.

Isaac Newton used Occam’s razor too when developing his theories. Newton stated: “We are to admit no more causes of natural things than such as are both true and sufficient to explain their appearances.” He sought to make his theories, including the three laws of motion, as simple as possible, with only the necessary minimum of underlying assumptions.

Medicine

Modern doctors use a version of Occam’s razor, stating that they should look for the fewest possible causes to explain their patient’s multiple symptoms, and give preference to the most likely causes. A doctor we know often repeats the aphorism that “common things are common.” Interns are instructed, “when you hear hoofbeats, think horses, not zebras.” For example, a person displaying influenza-like symptoms during an epidemic would be considered more likely to be suffering from influenza than an alternative, rarer disease. Making minimal diagnoses reduces the risk of over-treating a patient, causing panic, or causing dangerous interactions between different treatments. This is of particular importance within the current medical model, where patients are likely to see numerous health specialists and communication between them can be poor.

Prison Abolition and Fair Punishment

Occam’s razor has long played a role in attitudes towards the punishment of crimes. In this context, it refers to the idea that people should be given the least punishment necessary for their crimes. This is to avoid the excessive penal practices which were popular in the past. For example, a 19th-century English convict could receive five years of hard labor for stealing a piece of food.

The concept of penal parsimony was pioneered by Jeremy Bentham, the founder of utilitarianism. He held that punishments should not cause more pain than they prevent. Life imprisonment for murder could be seen as justified in that it might prevent a great deal of potential pain, should the perpetrator offend again. On the other hand, long-term imprisonment of an impoverished person for stealing food causes substantial suffering without preventing any.

Bentham’s writings on the application of Occam’s razor to punishment led to the prison abolition movement and many modern ideas related to rehabilitation.

Exceptions and Issues

It is important to note that, like any mental model, Occam’s razor is not foolproof. Use it with care, lest you cut yourself. This is especially crucial when it comes to important or risky decisions. There are exceptions to any rule, and we should never blindly follow the results of applying a mental model which logic, experience, or empirical evidence contradict. When you hear hoofbeats behind you, in most cases you should think horses, not zebras—unless you are out on the African savannah.

Furthermore, simple is as simple does. A conclusion can’t rely just on its simplicity. It must be backed by empirical evidence. And when using Occam’s razor to make deductions, we must avoid falling prey to confirmation bias. In the case of the NASA moon landing conspiracy theory, for example, some people consider it simpler for the moon landing to have been faked, others for it to have been real. Lisa Randall best expressed the issues with the narrow application of Occam’s razor in her book, Dark Matter and the Dinosaurs: The Astounding Interconnectedness of the Universe:

Another concern about Occam’s Razor is just a matter of fact. The world is more complicated than any of us would have been likely to conceive. Some particles and properties don’t seem necessary to any physical processes that matter—at least according to what we’ve deduced so far. Yet they exist. Sometimes the simplest model just isn’t the correct one.

This is why it’s important to remember that opting for simpler explanations still requires work. They may be easier to falsify, but still require effort. And that the simpler explanation, although having a higher chance of being correct, is not always true.

Occam’s razor is not intended to be a substitute for critical thinking. It is merely a tool to help make that thinking more efficient. Harlan Coben has disputed many criticisms of Occam’s razor by stating that people fail to understand its exact purpose:

Most people oversimplify Occam’s razor to mean the simplest answer is usually correct. But the real meaning, what the Franciscan friar William of Ockham really wanted to emphasize, is that you shouldn’t complicate, that you shouldn’t “stack” a theory if a simpler explanation was at the ready. Pare it down. Prune the excess.

Remember, Occam’s razor is complemented by other mental models, including fundamental error distribution, Hanlon’s razor, confirmation bias, availability heuristic and hindsight bias. The nature of mental models is that they tend to all interlock and work best in conjunction.

Albert Einstein on Sifting the Essential from the Non-Essential

Why is it so hard to sift the essential from the inessential? Few things have more of an impact on your life and career than your ability to zero in on what matters most. And yet most of us spend time cluttering our minds with things that don’t matter, rather than focusing on the simplicity that does.

It can feel difficult to keep up. There is an ever-increasing amount of information coming at us. Occasionally we get motivated and try to reach inbox zero but the onslaught doesn’t stop and we are soon back to where we started from. Efforts like this are well-meaning but misplaced, focusing on more and more effort instead of addressing the most important tool in our toolbox: our mind.

A lot of people think that Albert Einstein’s greatest ability was his mathematical mind. It wasn’t. Granted it’s probably better than yours or mine, but in comparison to his impact on the world most people in the know consider his mathematical gifts average at best.

Einstein’s greatest skill was the ability to sift the essential from the inessential — to grasp simplicity when everyone else was lost in clutter.

John Wheeler points out in his short biographical memoir on Einstein that it wasn’t that he understood more about complicated things that made him impressive:

Many a man in the street thinks of Einstein as a man who could only make headway in his work by dint of pages of complicated mathematics; the truth is the direct opposite. As Hilbert put it, “Every boy in the streets of our mathematical Gottingen understands more about four-dimensional geometry than Einstein. Yet, despite that, Einstein did the work and not the mathematicians.” Time and again, in the photoelectric effect, in relativity, in gravitation, the amateur grasped the simple point that had eluded the expert.

While it’s tempting to think that Einstein was born with this skill, that would be a lie.  In fact, it was developed consciously as an adult. “I soon learned,” Einstein said, “to scent out what was able to lead to fundamentals and to turn aside from everything else, from the multitude of things that clutter up the mind.”

“We have a passion for keeping things simple.”

— Charlie Munger

Where did Einstein acquire this ability to sift the essential from the non-essential? For this we turn to his first job.

In the view of many, the position of clerk of the Swiss patent office was no proper job at all, but it was the best job available to anyone with (Einstein’s) unpromising university record. He served in the Bern office for seven years, from June 23, 1902 to July 6, 1909. Every morning he faced his quote of patent applications. Those were the days when a patent application had to be accompanied by a working model. Over and above the applications and the models was the boss, a kind man, a strict man, a wise man. He gave strict instructions: explain very briefly, if possible in a single sentence, why the device will work or why it won’t; why the application should be granted or why it should be denied.

Day after day Einstein had to distill the central lesson out of objects of the greatest variety that man has power to invent. Who knows a more marvelous way to acquire a sense of what physics is and how it works? It is no wonder that Einstein always delighted in the machinery of the physical world—from the action of a compass needle to the meandering of a river, and from the perversities of a gyroscope to the drive of Flettner’s rotor ship.

Who else but a patent clerk could have discovered the theory of relativity? “Who else,” Wheeler writes, “could have distilled this simple central point from all the clutter of electromagnetism than someone whose job it was over and over to extract simplicity out of complexity.”

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The biggest mistake that most of us make is that we try to consume more information. We do this because we feel like we’re missing something. While we can all learn and improve our understanding of something, the constant search for what we don’t have and what we’re missing is also the natural response of someone who doesn’t truly understand what matters and what doesn’t. To understand what I mean consider investors.

The worst investors I know are focused on every news article, blog, or commentary on the company they own. Glued to their screen they look for some esoteric detail that others have missed. And because they are looking, they will eventually find something. Our brain convinces them that all of that effort paid off and they overvalue the new information. In fact, the vast majority of that time (9,999/10,000) that new bit of information won’t matter at all but they’ve lost the forest for the tree. Overvalued insight means unwarranted confidence. You can see where this is going.

On the other hand, the best investors I know focus only on company press releases and company filings. They know the few variables that truly matter and focus on monitoring those.

Simple. Effective. And efficient.

Clearly not every email in our inbox is important, not every moving part in a project will matter equally to the outcome, and not every opinion in a meeting is equally valid. We only have so much time. Giving things equal attention is not only inefficient but also ineffective.

Time is a great example of an overlooked simplicity. Sure, we learn a little bit about time in school. First we learn how to tell time and later we learn about time in the context of dates and speed. As we age, birthdays mark the passing of time. And of course, we have to be somewhere at a certain time, for a date, a flight, a graduation. That’s about the extent most of us will think about time until it’s too late.

Only when we’re older will we think about how we lived, what we worked on and who we worked with, and what mattered. Time is the simplicity before us that we ignore preferring to think about something more complex.

The skills to better filter and process are within our grasp: (1) focus on understanding basic, timeless, general principles of the world and use them to help filter people, ideas and projects; (2) take time to think about what we’re trying to achieve and the 2-3 variables that will most help us get there; (3) remove the inessential clutter from our lives; (4) think backwards about what we want to avoid.

5 Design-Informed Approaches to Good Learning

John Maeda offers five design-informed approaches for learning.

  1. BASICS are the beginning.
  2. REPEAT yourself often.
  3. AVOID creating desperation.
  4. INSPIRE with examples.
  5. NEVER forget to repeat yourself.

John Maeda is a graphic designer and computer scientist. His book, The Laws of Simplicity, proposes ten laws for simplifying complex systems in business and life. Think of it as simplicity 101.

Maeda has some interesting things to say on learning:

Learning occurs best when there is a desire to attain specific knowledge. Sometimes that need is edification, which is itself a noble goal. Although in the majority of cases, having some kind of palpable reward, whether a letter grade or a candy bar, is necessary to motivate most people. Whether there is an intrinsic motivation like pride or an extrinsic motivation like a free cruise to the Caribbean waiting at the very end, the journey one must take to reap the reward is better when made tolerable.

Maeda believes that the best motivator to learn is giving students a seemingly insurmountable challenge.

1. Basics are the beginning

The first step in conveying the BASICS is to assume the position of the first-time learner. As the expert, playing this role is not impossible, but it is best ceded to a focus group or any other gathering of external participants. Observing what fails to make sense to the non-expert, and then following that trail successively to the very end of the knowledge chain is the critical path to success. Gathering these truths is worthwhile but can be time consuming or else done poorly.

This echoes the first habit of effective thinking, understand deeply.

Be brutally honest about what you know and don’t know. Then see what’s missing, identify the gaps, and fill them in.

The easiest way to learn the basics is to teach them to yourself. Maeda tells this story to illustrate the point:

A few years ago, I visited the master of Swiss typographic design, Wolfgang Weingart, in Maine to give a lecture for his then regular summer course. I marveled at Weingart’s ability to give the exact same introductory lecture each year. I thought to myself, “Doesn’t he get bored?” Saying the same thing over and over had no value in my mind, and I honestly began to think less of the Master. Yet it was upon maybe the third visit that I realized how although Weingart was saying the exact same thing, he was saying it simpler each time he said it. Through focusing on the basics of basics, he was able to reduce everything that he knew to the concentrated essence of what he wished to convey. His unique example rekindled my excitement for teaching.

A quick way to figure out what basics you’re missing is the Feynman Technique.

2. Repeat yourself often. Repeat yourself often.

REPEAT-ing yourself can be embarrassing, especially if you are self-conscious-which most everyone is. But there’s no need to feel ashamed, because repetition works and everyone does it, including the US President and other leaders.

3. Avoid creating desperation

A gentle, inspired start is the best way to draw students, or even a new customer, into the immersive process of learning.

4. Inspire with examples

INSPIRATION is the ultimate catalyst for learning: internal motivation trumps external reward. Strong belief in someone, or else some greater power like God, helps to fuel belief in yourself and gives you direction.

5. NEVER forget to repeat yourself

forget to repeat yourself. Never Forget to repeat yourself. Never …