Think Better, Decide Smarter: The Mental Models Advantage

Mental models are the frameworks that shape our understanding of the world. They determine how we interpret information, make decisions, and solve problems. Simply put, mental models are the lenses through which we view reality—and as such, they influence every aspect of our lives.

Most people are more likely to see what they believe rather than believe what they see. The quote, "seeing is believing," suggests that if someone sees something, they are likely to believe it. But the reality is that we are programmed to see what we already believe.

But why exactly do mental models matter so much?

Simplifying Complexity

The world is overwhelmingly complex. With billions of variables influencing any moment, the human mind can't process everything simultaneously.

Mental models simplify this complexity by allowing us to focus on what is most relevant. They help us filter information, prioritize what matters, and navigate uncertainty with clarity. Without mental models, we would be paralyzed by the sheer volume of data we encounter every day.

Take the map-and-territory analogy: A map is a simplified representation of the terrain, highlighting only the features we need to navigate. Mental models function similarly. They allow us to distill intricate systems into manageable components, making it easier to make sense of the world.

However, just as an inaccurate map can lead you astray, flawed mental models can result in poor decisions. "The map is not the territory" is a metaphor that describes the difference between reality and our understanding of it.

The phrase means that our perception of reality is not reality itself but our own version of it. It's a reminder that models are a means to an end and require interpretation. We should not confuse models with reality.

Mental Models are not always accurate, but they are incredibly useful.

Enhancing Decision-Making

The quality of our decisions shapes the quality of our lives. Mental models provide the tools to analyze situations, predict outcomes, and choose the best course of action. When used effectively, they serve as a guide for navigating both personal and professional challenges.

For instance, consider the "Circle of Competence" mental model. By understanding the limits of our expertise, we can make better decisions about when to act and when to seek advice.

Similarly, models like "First Principles Thinking" and "Second-Order Thinking" help us challenge assumptions and anticipate long-term consequences. These frameworks prevent impulsive actions and enable more thoughtful, strategic choices.

Avoiding Cognitive Biases

Human thinking is riddled with biases—systematic errors in judgment that cloud our understanding and lead to suboptimal outcomes. Mental models act as counterweights to these biases. They help us recognize and mitigate blind spots, ensuring that our decisions are grounded in reality rather than distorted by assumptions or emotions.

One such bias is the Dunning-Kruger effect, where individuals with limited knowledge overestimate their competence. By adopting mental models that emphasize self-awareness and humility, such as "The Map is Not the Territory" or "Strong Opinions Weakly Held," we can guard against overconfidence and make more accurate assessments of our abilities.

Fostering Interdisciplinary Thinking

The most effective thinkers draw from a diverse set of mental models. Charlie Munger, the business partner of Warren Buffett, famously advocates for building a "latticework" of models from various disciplines. This approach enables us to see problems from multiple perspectives, uncovering insights that would otherwise remain hidden.

For example, an engineer approaching a business problem might rely on systems thinking, while a psychologist might focus on incentives and behavior. We can develop a more holistic understanding of the situation by integrating these different viewpoints.

This interdisciplinary approach not only enhances problem-solving but also fosters creativity and innovation.

Navigating Uncertainty

Life is unpredictable, and no decision comes with a guarantee of success. Mental models equip us with the tools to navigate uncertainty with confidence.

Probabilistic thinking, for instance, helps us assess risks and make informed choices despite incomplete information. Similarly, models like "Bayesian Updating" allow us to revise our beliefs as new evidence emerges, ensuring that our understanding evolves with changing circumstances.

In essence, mental models enable us to adapt. They provide the scaffolding for critical thinking, helping us remain agile and resilient in a rapidly changing world.

Improving Communication and Collaboration

Our mental models not only influence how we think but also how we communicate. By sharing and aligning models, we can bridge gaps in understanding and foster more productive collaboration. For instance, using common frameworks like "Pareto Principle" or "Eisenhower Decision Matrix" in a team setting ensures everyone is on the same page, streamlining decision-making and enhancing outcomes.

Moreover, mental models promote empathy. By recognizing the lenses through which others view the world, we can better understand their perspectives and find common ground. This skill is invaluable in both personal relationships and professional environments.

A Lifelong Pursuit

Building a robust set of mental models is a lifelong pursuit that requires curiosity, reflection, and continuous learning. The more models we accumulate, the more versatile and effective we become as thinkers and decision-makers.

But it's not just about adding new models—it's also about refining existing ones and knowing when to discard those that no longer serve us.

Ultimately, mental models matter because they empower us to take control of our lives. They help us see the world as it is, not as we wish it to be. They enable us to make better decisions, avoid pitfalls, and achieve our goals with greater clarity and purpose.

They are the best tools for navigating uncertainty and creating meaningful impact in a complex and ever-changing world.

A mental model is an internal representation of external reality: that is, a way of representing reality within one's mind. Such models play a major role in cognition, reasoning and decision-making.

Understanding mental models and how the impact our beliefs, behavior, and decisions help us better understand ourselves and the actions we take. 

And understanding of mental models also helps us understand others and their outlook and motivations. This ability to get into another person's mind helps us become better leaders.  It helps us motivate and get better work out of individuals and teams.

Listen to the lecture discussion and my book on mental models is attached as a downloadable PDF.

John Cousins's "Mental Models: Mastering the Art of Clear Thinking" introduces a framework of cognitive tools to enhance decision-making, problem-solving, and understanding the world.

The book explains that mental models simplify complexity, help avoid biases like the Dunning-Kruger effect, and encourage interdisciplinary thinking. It explores various models, including those related to general thinking, decision-making, analytical reasoning, productivity, humility, and strategic interaction using game theory. The text also discusses the relationship between mental models and heuristics, and emphasizes the importance of continuous learning and adapting one's thinking.

Ultimately, the book serves as a guide to building a "latticework" of mental models for clearer thought and more effective action in personal and professional life.

This video introduces the thinking tools, or mental models, championed by Charlie Munger, vice chairman of Berkshire Hathaway, emphasizing their importance for success in various aspects of life.

The Swedish Investor's YouTube series will explore specific models used by Munger, such as the "Swiss Army Knife Approach" of acquiring diverse knowledge and the strategy of "Invert, Always Invert" by focusing on avoiding negative outcomes.

The Twenty Percenter article further highlights five key mental models inspired by Munger, including the "Build Your Latticework" concept of connecting knowledge and the significance of "Follow Your Interests" for achieving mastery.

Both sources underscore the value of learning from others, even the deceased, and the necessity of challenging one's own deeply held beliefs to improve thinking and decision-making.

Mental models and heuristics are both tools our minds use to navigate complexity, but they serve different functions in our cognitive toolkit. Understanding their relationship—how they work together, where they differ, and their respective strengths and limitations—is essential for clear thinking.

Understand these mental models to make better investment decisions.

Since reading Charlie Munger's excellent book Poor Charlie's Almanack, I've been addicted to learning mental models.

There are many mental models out there, and you need to acquire a toolkit of 100 or so of the best ones to have a latticework to apply in your decision-making and investing.

You must familiarize yourself with the best microeconomics, behavioral, psychology, mathematics, science, and engineering models. Then, take the big ideas and learn them early and well.

The more mental models I learn, the more I replace my old ones with new ones. Questioning, unlearning, and updating our beliefs is a superpower.

Beliefs are hypotheses to be tested, not treasures to be protected.

I practice arguing like I'm right and listening like I'm wrong.

"Strong opinions, loosely held" is a concept attributed to Jeff Bezos of Amazon and is the idea that you should be able to present things you believe to be correct but willing to accept that you might be wrong. And be ready to adjust when you realize you are wrong.

Investing is about making predictions. Mental models help us make better, more informed predictions.

Here are ten examples of mental models I use for investing that help me identify promising companies and eliminate poor ones.

1. Bayesian Inference

Bayesian inference is a way of making guesses about things based on what you already know and what you learn as you go along.

It's like trying to guess what's inside a wrapped present by shaking it and feeling its weight. You might have an idea of what it could be based on its size and shape (your prior knowledge), but as you shake it and feel its weight (new data), you can update your guess and become more confident about what's inside (posterior probability).

This sort of thinking comes from famous statistician and Presbyterian minister Thomas Bayes.

When you use Bayesian inference in investing, you should continually update your predictions based on your information and the new information you receive.

This flexibility sounds counterintuitive because investing means putting a lump sum of money in the market and waiting for your investment to increase. Then, depending on your time horizon, you're either right or wrong with your prediction.

The right way to use Bayesian inference in investing is to do extensive research on a company and update your prediction often until it seems evident that a company is destined for success. You can calculate this as a statistic or rely on your gut feeling.

For example, Apple has been profitable for the past ten years. Of course, some years are better than others, but overall it has been growing in profit, and its gradual share price increase has increased in the market.

But continue beyond market data.

You can consider Apple's business model. It's to sell premium consumer hardware and services. Apple has a significant market share in that space, so you slightly raise the likelihood of a long-term competitive moat. Balance this with supply chain issues with China so you decrease your prediction. Then, increase it again by understanding Apple's music market.

Warren Buffet balanced all these factors when investing in Apple, and you should too.

A tip on Bayesian inference is not to over and under-react to information but to strive for a balance.

"The individual who knows how little they know about themselves stands the most reasonable chance of finding out something about themselves before they die."- S.I. Hayakawa.

Bayesian inference is a statistical method in which Bayes' theorem is used to update the probability of a hypothesis as new evidence or information becomes available.

In other words is a mathematical model for revising and updating based on acquiring new knowledge and information. It's a core concept in artificial intelligence.

The Lean Startup Method calls it iteration. Entrepreneurial iteration is course correcting in the face of feedback.

"True life is lived when tiny changes occur."

- Leo Tolstoy

We can incorporate a Bayesian mindset and update our opinions and beliefs as we encounter new information. We can be fluid in our thinking and respond to those new contours with revised opinions as our understanding evolves. We can change our minds in light of new information.

Put another way, Bayesian inference is an iterative technique for refinement and asymptotically approaching a goal. Just get a dart on the dartboard, then iterate toward the bullseye.

Here's what Elon Musk has to say on the subject,

"I think it's very important to have a feedback loop, where you're constantly thinking about what you've done and how you could be doing it better. I think that's the single best piece of advice: constantly think about how you could be doing things better and questioning yourself. This is the way we refine our ideas and become more enlightened."

2. Put a Probability to it

The best way to be a more accurate decision-maker is to put a probability to your guesses.

Robert Rubin ran Goldman Sachs and was the seventieth U.S. Secretary of the Treasury. Rubin's fundamental philosophy is that nothing is provably certain. Probabilistic thinking has guided his career in both business and government.

According to Philip Tetlock, author of Superforecasting, putting a probability to your predictions helps you be more accurate and accountable.

Probability is how likely something is to happen. So whenever we're unsure about the outcome of an event, we can talk about the probabilities of certain outcomes — how likely they are. The analysis of events governed by probability is called statistics.

Putting a number to something you don't know about is difficult. For example, what is tomorrow's weather forecast?

You could say it'll be raining.

But, if you put a number to it, you'll mentally work harder to be more accurate. So, instead, you might say you are 50% confident it'll be raining tomorrow, or, if you experienced a streak of sunny days, so you might say instead you're 30% convinced it'll rain tomorrow.

It's not about the number being on point but developing a ballpark figure on your prediction confidence.

The goal here is to apply it to Bayesian inference.

Let's think about Apple again.

Apple's consistent profit gives us 80% confidence for future growth. You keep the number at 80% because it aligns with Apple's large consumer market. However, as you learn about Apple's supply chain issues, you'll lower your prediction to 75%. Then, you'll raise it back to 78% because you've read that Apple has diversified its supply chain to India.

Putting a number to your predictions is more of an art than a science, but the more you do it, the more accurate your predictions will become.

But Remember:

No matter how clever our decisions are or how successful we are at controlling the odds, chance will ultimately prevail.

Probability is not just the calculation of dice odds or more complex variations; it is the acknowledgment of the uncertainty in our knowledge and the development of strategies for dealing with our ignorance.

3. Circle of Competence

Warren Buffett developed the concept of the Circle of Competence.

The circle of competence is a symbolic boundary defining the knowledge and expertise an individual or organization profoundly understands. Within this boundary, individuals or organizations can make accurate predictions, assess risks more effectively, and make informed decisions based on their knowledge and experience.

The biggest hindrance to making good predictions is making a prediction based on a few sources of information. For example, the annual report will always paint a company in a favorable light, market analysis will generally show a positive trend for most industries, and news articles can inadvertently make you overreact to information.

A mistake in developing a circle of confidence is believing that cramming a lot of information quickly will build confidence.

Build confidence in industries such as technology by reading annual and industry reports. Research the tech industry and become broadly with trends such as CAPEX, costs of data centers, software development, and developing consumer demands.

It takes continuous learning to develop a decent degree of confidence in any field. You'll gain confidence faster in similar areas, for example, software and hardware development, where overlap exists.

4. Accounting

"Accounting is the language of business." — Warren Buffett.

Goethe called double-entry bookkeeping "one of the finest inventions of the human mind."

Accounting is the process of recording, summarizing, and presenting financial transactions and information.

Learning accounting is like a new language; each new topic you learn with accounting will be difficult to understand initially but becomes easier over time.

It'll take you some time to learn the vocabulary, such as balance sheet, income statement, cash flows, etc., and then you need to grasp what the words mean in the context of their industry.

For example, large mining companies like to use EBITDA in their reporting. However, it's not representative of their operating income, so why use it? The reason is that depreciation and amortization are slow expenses in the mining industry, where mines can last for years. This timeframe means the depreciation and amortization expenses don't need to be "paid" for years.

If you applied EBITDA to the tech industry, it would make less sense because tech companies make money from their software more than from producing products from equipment. So use EBIT in this case.

Here's a more recent example. Warren Buffett announced he would exit several positions in banks because of the way they record cost bonds. Banks have been recording their bond holdings at the cost they bought them for instead of the current market cost. Because of the interest rate increase, any bonds purchased while interest rates were low are worth less today. This scenario is what led to the collapse of Silicon Valley Bank. You can quickly discover this just by reading the banks' accounting policies.

Accounting is both exciting and boring to learn. Accounting is a headache when it makes no sense, but once you get the hang of it, it's like reading an exhilarating book.

5. Opportunity Costs

Opportunity cost is the value you lose when you choose one option over another. It is the best alternative that you could have chosen. It is also the profit lost when you invest in one asset instead of another.

Applying opportunity cost to investing is as simple as "Should I invest A over B?" Although, I admit it's far easier to say than do.

For example, countless times, I felt I had found a good investment, but I resisted the urge to invest and kept looking to find better ones.

Other times, I have two perceived equally good opportunities, and I'm tempted to split my investment in them. That may not be a bad decision as it adds diversity.

The most logical way to choose between several investment choices is to calculate the discounted cash flows for each option and choose the best. But, the gut feeling often is the deciding factor.

I calculate the discounted cash flows of each option and let my intuition help me choose the best option.

It's tempting to let someone else analyze you, that's why we have so many financial analysts, but you will rob yourself of understanding why some opportunities are better than others.

6. Mr. Market

Mr. Market is an allegory created by investor Benjamin Graham to describe the irrational or contradictory traits of the stock market and the risks of following groupthink. In his book, The Intelligent Investor, Graham describes Mr. Market as a hypothetical investor driven by panic, euphoria, and apathy. Mr. Market is an imaginary investor with an irrational yet predictable behavioral pattern—predictably irrational.

Mr. Market will offer you a low price for your stock holdings one day, then a high price the next day. Once in a while, he'll provide you with a fair price.

The allegory is a warning not to trust the market as being rational in the short term. It is a critique of the efficient market hypothesis. However, Graham does say that in the long term, Mr. Market will offer a fairer, rational price for a company.

"In the short run, the market is a voting machine, but in the long run, it is a weighing machine." — Benjamin Graham.

If you look at Apple's operating profit on a long-term horizon, you also see a correlation between share price and operating performance.

I keep my time horizon long to counter the short-term gyrations of Mr. Market. My goal is to hold onto companies for years, but I make sure that the company's prospects and share price are also correlated.

However, sometimes Mr. Market can be your friend. For example, if you believe your investment's operations are deteriorating, but Mr. Market is offering you a good price, you may sell at a profit. Or you may find opportunities to buy the dip when a stock gets hammered.

7. Behavioral Economics

Behavioral economics combines elements of economics and psychology to understand how and why people behave the way they do in the real world. It studies the effects of psychological, cognitive, emotional, cultural, and social factors on the decisions of individuals or institutions.

The basic premise is a reaction to classical economics abstractions about how people are entirely rational in their decision-making.

Behavioral economics is concerned with the bounds of rationality of economic agents. Behavioral models typically integrate insights from psychology, neuroscience, and microeconomic theory.

Investors should understand behavioral economics not so much to understand how others make decisions but more to know how you make decisions. We need to be aware of our blind spots and cognitive biases.

For example, if a company's share price suddenly jumps, many investors want to invest because of the fear of missing out. They fear the stock price will be greater tomorrow if they don't invest now. This behavior is a combination of the scarcity principle and anchoring.

I've been guilty of this as well. I bought a particular company while its share price was going up, just for it to halve in value a few weeks later. I feared the share price would skyrocket after good half-yearly results.

You can use behavioral economics to make an educated guess of how others might think, enabling you to take advantage of market irrationalities. Of course, this is easier said than done.

Here are some examples to get you started:

• Anchoring — People will buy or sell according to whatever price they see. If they see that Apple has sold between $130 and $170 in the last six months, they may hold out on buying at $170 and wait until $130.

• Availability Heuristic — People invest primarily in what they know now and forget about the past. For example, Microsoft shot up from $250 to $300 in the last three months after the success of ChatGPT. But Microsoft's interest in A.I. shouldn't come as a surprise. Satya Nadella in his 2017 memoir, Hit Refresh, talked about Microsoft wanting to create A.I. products. More recently, OpenAi, into which Microsoft has invested billions, was successful with the viral image creation product DALL-E. I say this out of the benefit of hindsight. I didn't see it coming but someone with a deep circle of competence in A.I. could have.

• Inconsistency Avoidance — Most managers sincerely try to do as they say. They will also try to stay in their set ways even if those ways are no longer productive. For example, BHP committed itself to being a green mining company, and it did so by selling off its petroleum assets. BHP is staying consistent with its goals, but the way they did it neither helps the environment nor its profits. Instead, it simply assured ESG investors.

The best way to describe behavioral economics is by putting a label on stupid behavior, especially your own.

8. Policies and Rules

People often believe you can achieve unique insights by applying statistical modeling to numbers. Unfortunately, I don't think this is always the case. After working as a data scientist, I know that modeling helps you digest the data but won't give you special insights.

I say this because data is the numeric output of rules and policies. In other words, the numbers you see in financial tables or large data sets are what they want you to see.

Let's use financial statements as an example. I was naive when I first started investing. I thought all I needed to do was find companies making a profit every year, skim the financial statements to find any irregularities, and put some values into a discounted cash flow model to calculate intrinsic value.

However, after reading the book Financial Shenanigans and The Essays of Warren Buffett, I realized I was doing value investing incorrectly. The financial statements are whatever the company management wants you to see, and they have great incentive to show you a profit-producing company.

The auditors aren't going to question the managers because they pay the auditors; instead, the auditors record what the managers want to say and sign off that the managers aren't hiding anything.

Here's a simple trick managers can use to trick investors. Companies can reduce their expenses by recognizing option prices at a share price dip, not the current cost. This recording means company profits look higher when the time comes for the annual report. The management team can even write this into policy; the auditors don't need to question it.

If you were modeling, this company's intrinsic value would be much higher than if you factor in the management team's big paycheck.

You also get other adverse effects. For example, expensing at a lower share price hides how much the management team's compensation dilutes your holdings.

Don't trust numbers you haven't gathered yourself; instead, they're a good place to start to understand what people want to communicate, and you can judge from there if someone is trying to scam you or giving you an honest reflection of a potential investment.

9. Competitive Advantage

Competitive advantage is an attribute that allows an organization to outperform its competitors. It can include access to natural resources, skilled labor, geographic location, high entry barriers, and access to new technology and proprietary information. To gain and maintain a competitive advantage, an organization must demonstrate greater comparative or differential value than its competitors and convey that information to its target market. A competitive advantage sets a company apart from its competitors and allows it to achieve superior margins, a better growth profile, or greater customer loyalty.

Investing involves being proficient at reading accounting numbers and knowing that your investment can out-compete the competition.

Recognizing a single competitive advantage of a company is easy, but identifying the various other factors that make a company superior to its competitors is challenging.

For example, most people would say McDonald's competitive advantage is its world-renowned Golden Arches trademark and the consistency of its burgers globally. But there's more to it.

The McDonald's company's high profit margin is high because it plays more of a real estate landlord role than a company that provides fast food. McDonald's can maintain consistency because it will only take on franchisees who will commit to 20-year contracts. All McDonald's store layouts are designed to be attention-grabbing yet funnel your decision-making to high-profit items.

A company's competitive advantage is built on many factors, some of which are only sometimes apparent.

Suppose you can find a company with a solid competitive advantage, or like what Warren Buffett likes to say, a competitive moat, and combine that with strong profits and good accounting. In that case, you have found a winning company.

10. Noise

Noise in decision-making is the random variation in judgments that impairs the accuracy of decisions. It is a type of error that can occur in any decision-making process, regardless of the level of expertise of the decision-maker.

The investing landscape is noisy. You have all sorts of information, such as ticket data, analyst reports, and news reports. If you combine all these, you won't know whether or not you should invest in a company because they will conflict with each other.

What matters in investing is making a simple decision and being selective in what you want to know.

For example, I've learned that business data analysis only matters in investing a little because it's messy and doesn't translate into profits easily.

A company might have products sold globally, but this means you will need to factor in different exchange rates. For example, are 100 iPhones sold in Australia comparable to 100 iPhones sold in the U.S.?

As of this writing, in Australia, an iPhone 14 costs $1400, which is USD 934.

In the U.S., an iPhone 14 starts at $799.

Reading accounting numbers is better, but they are subject to management's discretion, so you need to trust you're getting the most accurate representation of the company.

Then you need to contend with new reports. For instance, generative A.I. might sound like it'll replace the jobs of all tech workers. So, big companies should now begin seeing cost savings from laying off workers.

Simplistic investors will cheer for this and pump money into such companies because of perceived cost savings. However, second-order thinking will make you think, will a company need to increase existing workers' salaries? Or, how much does A.I. really cost?

The decision just got noisier when you included tough questions.

My observations show that people like to make decisions in noisy environments. This is because they want to look like the smart ones making the right choices in demanding environments.

I shy away from this. You're better off profiting from simple situations that guarantee results regardless of the hype and noise.

For example, if the stock market falls and everything falls with it, you're better off pouring your money into an index fund at rock bottom prices than into companies where you don't have competence. You should do this because it's simple, you're investing in the top 200 to 500 companies, and stock prices generally go up over a long-time frame.

You have tactics to overcome noise, such as overcoming your biases, using simple algorithms to optimize decision-making, and sticking with accepted decision-making policies.

In investing, the best way to overcome noise is by picking easy and apparent targets that produce reasonable returns on investment.

Conclusions

If you're a student of Warren Buffett, then you'll realize all the mental models I've just described are what he uses. Just read his annual letters, and you'll understand what I mean.

After going on about the usefulness of mental models in decision-making, here is a caveat for relying too heavily on mental models. A map is not the terrain. Likewise, a model is not the world.

This list is just the beginning. I'm exposing you to what mental models work well in investing, but you'll need to dig further into them to understand how to use them. Then, you'll need to do the hard work yourself.

* * *

Japanese Mental Models for Mental Clarity and Calmness

Six Japanese mental models can help achieve mental clarity and calmness in today's chaotic world.

Introduction

A Zen story illustrates how an overfilled teacup represents a mind too full to receive new teachings. Similarly, our minds today are overwhelmed with stress and noise, making it difficult to find clarity.

The Six Mental Models

1. Ikigai - Your Reason for Being

Ikigai goes beyond temporary passion and consists of four key questions:

• What do you love?

• What are you good at?

• What does the world need?

• What can you get paid for?

Finding yourself isn't about consistency but about noticing what energizes you in the present moment.

2. Kaizen - Continuous Small Improvements

Kaizen represents gradual, steady improvements that accumulate over time. Genuine kaizen differs from superficial productivity tactics:

• Simply collecting self-help information isn't real growth

• Using techniques like bullet journaling won't resolve deeper regrets

• Many people reorganize minor aspects of their lives instead of addressing fundamental issues

• Pretending to be fine doesn't make it so

True kaizen involves making small, consistent improvements daily—a process that's "unsexy, slow, and not very Instagrammable."

3. Shikata ga nai + Ma - Acceptance and Space

Shikata ga nai means "it cannot be helped" and teaches acceptance of what cannot be changed. Instead of fighting unchangeable circumstances, this concept encourages working with what you have.

Key insights include:

• Not everything has a solution

• Not all problems require your intervention

• Silence can be more powerful than action

• Complete control is an illusion

This philosophy creates space (Ma) where you can breathe between effort and outcomes.

4. Wabi-Sabi - Beauty in Imperfection

Wabi-sabi embraces imperfections as features rather than flaws. In contrast to a culture obsessed with self-improvement and optimization, this concept teaches that:

• Flaws are distinctive features

• Imperfections tell meaningful stories

• "Enough" is an unattainable concept

• Your messy aspects deserve highlighting

A personal example involves embracing a distinctive laugh instead of trying to make it more "elegant."

5. Zanshin - Continued Awareness

Zanshin refers to the attention maintained after completing an action—the stillness following achievement. Most people struggle with this concept, as they:

• Rush through tasks without mindfulness

• Collapse exhausted after completion

• Release work hastily without proper review

This concept encourages maintaining awareness beyond the completion of tasks.

6. Yugen - Ineffable Depth

Yugen represents experiences that defy simple explanation or categorization. These are feelings that cannot be reduced to bullet points or social media content.

On a withered branch

A crow is perched

An autumn evening

Basho's haiku about a crow on a bare branch illustrates yugen, evoking complex emotions beyond simple categorization. Yugen is described as "the space between" joy and sadness, comparable to the feeling of hearing a forgotten beloved song or being awake at 3 AM with a sense of peaceful wakefulness.

Here are thoughts from Dr. Terrence Sejnowski, a top learning expert. He is a pioneer in computational neuroscience, and one of a handful of scientists elected to all three US national academies: engineering, science, and medicine. Here is a list of significant takeaways and tips about learning:

• There is a general principle that you can learn more through active engagement, independently solving problems, practical experimentation, or participation in a discussion than passive listening. This concept is the notion of learning by doing and can sometimes be more effective than simply reading many books.

• Learning by osmosis from more knowledgeable people is another good way to assimilate information. Also, being in a creative environment around other creative people is a potential way to enhance your creativity and productivity. You can improve the quality and clarity of your ideas if you have other people to bounce them off or try explaining them to (The Feynman Technique).

• Passion and persistence can beat pure intelligence in the pursuit of success.

• Exercise such as running can effectively disengage the mind from regular trains of thought and help develop new ideas through the diffuse mode. In essence, it can allow your subconscious thoughts to bubble to the surface.

• It is of great difficulty, if not impossible, to consciously do or focus on two or more things at once, as they’re likely to become mixed up. Multitasking is more a case of ‘context switching’ between topics.

• Try not to get hung up on a question you can’t answer in a test environment. Instead, move on to the next question. Often, the answer to the problem holding you back can mysteriously pop into your brain later in the test, courtesy of the diffuse mode. Our brains can operate with disparate things working on parallel tracks.

• Neurological discoveries have revealed that the hippocampus (a seahorse-shaped part of the brain that is paramount in learning and memory, located in the middle) continually generates new neurons, even well into adulthood. Studies of rats tell us that having an ‘enriched environment’ such as the freedom to move around and interact with things and people, encourages the formation of much stronger neural connections. You ideally want to be surrounded by other people who are stimulating you and have access to events in which you can actively participate.

• However, independent of such an enriched environment, exercise can also boost the number of new neurons that are born and survive in your hippocampus, aiding you in remembering things.