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The Great Mental Models, Volume 1: General Thinking Concepts

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The Great Mental Models, Volume 1: General Thinking Concepts

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Highlights & Notes

The way Munger described it, these mental models help identify what information is relevant in any given situation and the most reasonable parameters to work within.

if you want to suck up someone’s brain, you should simply read a book. All the great wisdom of humanity is written down somewhere.

we can benefit from understanding how the world works and apply that understanding to keep us out of trouble.

It is remarkable how much long-term advantage people like us have gotten by trying to be consistently not stupid, instead of trying to be very intelligent. —Charlie Munger

In life and business, the person with the fewest blind spots wins. Blind spots are the source of all poor decisions.

Reducing blind spots means we see, interact with, and move closer to understanding reality. We think better. And thinking better is about finding simple processes that help us work through problems from multiple dimensions and perspectives, allowing us to better choose solutions that fit the objective.

Decisions based on improved understanding will be better than ones based on ignorance.

best: “I don’t want to be a great problem solver. I want to avoid problems—prevent them from happening and do it right from the beginning.”[1]

We must understand how the world works and adjust our behavior accordingly.

Mental models describe the way the world works. They shape how we think, how we understand, and how we form beliefs. Largely subconscious, mental models operate below the surface. We’re not generally aware of them, and yet when we look at a problem, they’re the reason we consider some factors relevant and others irrelevant. They are how we infer causality, match patterns, and draw analogies. They are how we think and reason. A mental model is a compression of how something works. Any idea, belief, or concept can be distilled down. Like maps, mental models reveal key information while ignoring the nonessential.

While it helps to think of each model as a map, collectively they act as lenses through which you can see the world. Each lens (model) offers a different perspective, revealing new information. Looking through one lens lets you see one thing, and looking through another reveals something different. Looking through them both reveals more than looking through each one individually.

Not having the ability to shift perspective by applying knowledge from multiple disciplines makes us vulnerable. Mistakes can become catastrophes whose effects keep compounding, creating stress and limiting our choices.

The more lenses used on a given problem, the more reality reveals itself. The more of reality we see, the fewer blind spots we have. The fewer blind spots we have, the better the options at our disposal.

When understanding is separated from reality, we lose our powers to make better decisions. Understanding must constantly be tested against reality and updated accordingly.

If you don’t test your ideas against the real world—if you don’t keep contact with the earth—how can you be sure you understand it? While pontificating with friends over a bottle of wine at dinner can be fun, the only way you’ll know the extent to which you understand reality is to put your ideas into action.

The biggest barrier to learning from the world is ourselves. It’s hard to understand a system that we are part of because we have blind spots, where we can’t see what we aren’t looking for and don’t notice what we don’t notice.

Our failures to update our mental models as we interact with the world spring primarily from three factors: not having the right perspective or vantage point, ego-induced denial, and distance from the consequences of our decisions.

The first flaw is failure of perspective. We have a hard time seeing any system that we are a part of. We think our angle of perception is the right one and the only one.

If we truly want to understand the results of our actions, we must be open to other perspectives. Allowing for other perspectives is also key to having productive relationships with others. The second flaw is ego—the

Many of us tend to have too much invested in our opinion of ourselves to see the world’s feedback—the feedback we need to update our beliefs about reality.

The third flaw is distance. The further we are from the results of our decisions, the easier it is to maintain our current views rather than update them.

Large organizations often remove us from the direct consequences of our decisions. When we make decisions that other people carry out, we are one or more levels removed from their consequences and may not immediately be able to update our understanding—we come a little off the ground, if you will. The further we are from the feedback on our decisions, the easier it is to convince ourselves that we are right and avoid the challenge, the pain, of updating our views.

“A man who has committed a mistake and doesn’t correct it, is committing another mistake.”

We also tend to undervalue elementary ideas and overvalue complicated ones.

In an effort to ensure that our contributions are unique, we often end up rejecting simple solutions and focusing instead on complexity. But simple ideas are of great value because they can help us prevent complex problems.

Understanding becomes useful only when we adjust our behavior and actions accordingly. The Great Mental Models are not just theory; they are actionable insights that can be used to create positive change in your life. What good is it to know that you constantly interrupt people, if you fail to adjust your behavior in light of this understanding? Granted, recognizing a mistake is easier than changing our behavior, since behavior patterns tend to be ingrained. It takes effort to change behavior, but your effort will be well spent. Don’t give up; change requires consistency. If you stick with it, you’ll see the fruits of your new understanding and its many downstream effects in real life.

Now you can see how we make suboptimal decisions and repeat mistakes: We are afraid to learn and to admit when we don’t know enough. This is the mindset that leads to poor initial decisions.

We get tripped up when the world doesn’t work the way we want it to or when we fail to see what is. We end up negotiating with reality, in a fight we are sure to lose; we think the world should work the way we want it to rather than the way it does. Instead of updating our views, we double down on our effort, accelerating our frustration and anxiety.

We need to learn to understand when ego serves us and when it hinders us. When we strive more toward outcomes rather than personal status—especially if those outcomes benefit more people than ourselves—that’s a good use of ego.

The world is smarter than we are and it will teach us all we need to know if we’re open to its feedback—if

Better models mean better thinking. The more accurately our models explain reality, the more they improve our thinking. Understanding reality is the name of the game. Understanding not only helps us decide which actions to take but helps us avoid actions that have a big downside that we otherwise would not be aware of. When we understand, not only do we see the immediate problem with more accuracy, we can also begin to see the second-, third-, and higher-order consequences of various choices. This understanding helps us eliminate avoidable errors. Sometimes, making good decisions boils down to avoiding bad ones.

We compound the problem of flawed models when we fail to update our models after evidence indicates they are wrong. Only by repeatedly testing our models against reality and being open to feedback can we update our understanding of the world and change our thinking. We need to look at the results of applying a model over the largest sample size of problems possible to be able to refine it so that it aligns with how the world actually works.

A lot of people start out with 400-horsepower motors but only get a hundred horsepower of output. It’s way better to have a 200-horsepower motor and get it all into output. —Warren Buffett[9]

Better models make better decisions.

Making mistakes is part of the process of using mental models. Failing, if you acknowledge, reflect on, and learn from it, is also how you build mastery.

When you start to understand the world better, when the whys become less mysterious, you will gain confidence in how you navigate. Successes will accrue. More success means more time, less stress, and, ultimately, a more meaningful life.

Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful. —George E. P. Box[1]

Relying solely on maps can lead you to the wrong conclusion. You need to touch the territory.

“When the data and the anecdotes disagree,” he said in an interview, “the anecdotes are usually right.”[2] At the meeting, the head of customer service reported the wait-time metric as under sixty seconds, which was in line with expectations. Bezos paused the meeting, picked up the phone, and dialed the 1-800 number for customer service. He waited on hold for over ten minutes, which made the point: something was wrong with the data collection.

Mental models and maps are both useful to the extent they are explanatory and predictive.

The only way we can navigate the complexity of the world is through some sort of abstraction.

We inadvertently forget that the map is not reality. It’s the illusion of knowledge.

We need maps and models as guides. But frequently, we don’t remember that our maps and models are abstractions, and thus we fail to understand their limits. We forget there is a territory that exists separately from the map. This territory contains details the map doesn’t describe. We run into problems when our knowledge becomes knowledge of the map rather than of the actual underlying territory it describes.

If the value of a map or model is related to its ability to predict or explain, then it needs to represent reality. If reality has changed, the map must change.

Any user of a map or model must realize that we do not understand a model, map, or reduction unless we understand and respect its limitations. If we don’t understand what the map does and doesn’t tell us, it can be useless or even dangerous.

In order to use a map or model as accurately as possible, we should take into account three important principles: Reality is the ultimate update. Consider the cartographer. Maps can influence territories.

But territories change, sometimes faster than the maps and models that describe them. We can and should update our maps based on our own experiences in the territory. That’s how good maps are built: through feedback loops created by explorers.

Consider the cartographer: Maps are not purely objective creations. They reflect the values, standards, and limitations of their creators.

Models are most useful when we consider them in the context in which they were created. What was the cartographer trying to achieve? How does this influence what is depicted in the map?

Updating our maps is a difficult process of reconciling what we want to be true with what is actually true. In many areas of life, we rely on maps offered by others. We depend on the maps provided by experts, pundits, and teachers. In these cases, the best we can do is to choose our mapmakers wisely, to seek out those who are rigorous, transparent, and open to revision. Ultimately, the map-territory distinction is an invitation to engage with the world as it is, not just as we imagine it to be. And remember, when you don’t make the map yourself, choose your cartographer wisely.

I’m no genius. I’m smart in spots—but I stay around those spots. —Thomas J. Watson[1]

Understanding where you have an edge in competence and where you don’t helps you prevent problems, spot opportunities, and learn. We all have a circle of competence—an area in which we have a lot of knowledge. The size of that circle is not as important as knowing when you are approaching its perimeter. Within your circle of competence, you operate with a clear advantage. As you approach the perimeter (the limitations of your knowledge), your advantage starts to decrease. As you cross the perimeter, not only does your advantage vanish, it goes negative. Suddenly, you find yourself playing in an area where others have an advantage.

True knowledge of a complex territory cannot be faked.

if you don’t have at least a few years and a few failures under your belt, you cannot consider yourself competent in a circle.

I never allow myself to have an opinion on anything that I don’t know the other side’s argument better than they do. —Charlie Munger[6]

A little learning is a dangerous thing; Drink deep, or taste not the Pierian spring: There shallow draughts intoxicate the brain, And drinking largely sobers us again.[7]

There is no shortcut to understanding. Building a circle of competence takes years of experience, of making mistakes, and of actively seeking out better methods of practice and thought.

There are three key practices needed to build and maintain a circle of competence: curiosity and a desire to learn, monitoring, and feedback.

First, you have to be willing to learn. Learning comes when experience pairs with reflection. Experiences can be yours or those of others, absorbed through books, articles, and conversations. Learning everything on your own is costly and slow. You are one person. Learning from the experiences of others is much more productive. The key to learning is reflecting on those experiences and compressing them into something usable. You need to approach your circle of competence with curiosity, seeking out information that can help you expand and strengthen it.

I want to think about things where I have an advantage over other people. I don’t want to play a game where people have an advantage over me…. I don’t play in a game where other people are wise and I am stupid. I look for a game where I am wise and they are stupid. And believe me, it works better. —Charlie Munger[8]

Ego is a powerful enemy when it comes to better understanding reality.

You can’t improve if you don’t know what you’re doing wrong.

Finally, you must occasionally solicit other perspectives. This helps build a circle of competence but is also critical for maintaining one.

Atul Gawande is one of the top surgeons in the United States. When he wanted to get better at being a surgeon, he hired a coach. This is terribly difficult for anyone to do, let alone a doctor. At first, Gawande felt embarrassed. It had been over a decade since he was evaluated by another person, in medical school. “Why,” he asked, “should I expose myself to the scrutiny and fault-finding?”[9] The coaching worked. Gawande later wrote that he got two things out of this experience. First, he received information about something he couldn’t see himself and that no one else would point out (if they noticed it at all): knowledge of where his skill and technique were suboptimal. The second thing he gained was the ability to provide better feedback to other doctors. It is extremely difficult to maintain a circle of competence without an outside perspective. We usually have too many biases to rely solely on our own observations. It takes courage to solicit external feedback, so if you notice yourself start to manifest defensiveness, focus instead on the result you hope to achieve.

She kept the group small, so that real discussions could happen. She wanted a variety of opinions that could be challenged and debated.[12]

The first rule of competition is that you are more likely to win if you play where you have an advantage. Doing so requires a firm understanding of what you know and don’t know. Your circle of competence is your sphere of expertise, the area where your knowledge and skills are concentrated. It’s the domain where you have a deep understanding, where your judgments are reliable, and your decisions are sound. The size of your circle isn’t as important as knowing the boundaries. A wise person knows the limits of their knowledge and can confidently say, “This falls within my circle,” or “This is outside of my expertise.”

Ignorance more often begets confidence than knowledge. —Charles Darwin[13]

Applying the filter of falsifiability helps us sort through which theories are more robust. If a theory can’t ever be proven false, because we have no way of testing it, then the best we can do is try to determine its probability of being true.

I don’t know what’s the matter with people: they don’t learn by understanding; they learn by some other way—by rote or something. Their knowledge is so fragile! —Richard Feynman[1]

First principles thinking is one of the best ways to discover new solutions. Sometimes called “reasoning from first principles,” it’s a tool to help break down complicated problems by separating what we know is absolutely true from anything that is an assumption. What remain are the essentials. If you know the first principles of something, you can build the rest of your knowledge around them to produce something new.

Socratic questioning generally follows this process: Clarifying your thinking and explaining the origins of your ideas. (Why do I think this? What exactly do I think?) Challenging assumptions. (How do I know this is true? What if I thought the opposite?) Looking for evidence. (How can I back this up? What are my sources?) Considering alternative perspectives. (What might others think? How do I know I am correct?) Examining consequences and implications. (What if I am wrong? What are the consequences if I am?) Questioning the original questions. (Why did I think that? Was I correct? What conclusions can I draw from the reasoning process?)

Science is much more than a body of knowledge. It is a way of thinking. —Carl Sagan[3]

Understanding how and why something works is a key step to improving it. First principles thinking helps us avoid the problem of relying on someone else’s tactics without understanding the rationale behind them.

animals. If you know the principles, you can change the tactics.[8]

The real power of first principles thinking is moving away from random change and into choices that have a real possibility of success.

As to methods, there may be a million and then some, but principles are few. The man who grasps principles can successfully select his own methods. The man who tries methods, ignoring principles, is sure to have trouble. —Harrington Emerson[9]

First principles thinking is the art of breaking down complex problems into their most fundamental truths. It’s a way to see beyond the surface and gain a new perspective. By reasoning from first principles, we identify root causes, strip away layers of complexity, and focus on the most effective solutions. It allows us to step outside the way things have always been done and, instead, see what is possible. First principles thinking is not easy. It requires a willingness to challenge the status quo. That’s why it’s often the domain of rebels and misfits who believe there must be a better way. It’s the mindset of those willing to start from scratch and build from the ground up. In a world focused on incremental improvement, first principles thinking offers a competitive advantage because almost no one does it.

Creativity is intelligence having fun. —Anonymous[1]

Thought experiments can be defined as “devices of the imagination used to investigate the nature of things.”[2]

We can use thought experiments to reveal blind spots.

Thought experiments are more than daydreaming. To be useful, they require the same rigor as a traditional experiment. Much like the scientific method, a thought experiment generally has the following steps: Ask a question. Conduct background research. Construct a hypothesis. Test with (thought) experiments. Analyze outcomes and draw conclusions. Compare to hypothesis and adjust accordingly (new question, etc.).

One of the real powers of the thought experiment is that there is no limit to the number of times you can change a variable to see if it influences the outcome.

a few areas in which thought experiments are tremendously useful. Imagining physical impossibilities Reimagining history Intuiting the nonintuitive

doing the thought experiment—is what leads to insights regarding what we value in life and where to focus our energies.

These approaches are called the historical counterfactual and semifactual. If Y happened instead of X, what would the outcome have been? Would the outcome have been the same?

One of the goals of a thought experiment like this is to understand the situation enough to identify the decisions and actions that had impact.

Thus, by exploring the realistic relationships between events, you can better understand the most likely effects of any one decision.

Thought experiments are the sandbox of the mind, where we can play with ideas without limits. They’re a way of exploring the implications of our theories, pushing the boundaries of our understanding. They offer a powerful tool for clarifying our thinking, revealing hidden assumptions, and uncovering unintended consequences. The power of thought experiments lies in their ability to create a simplified model of reality where we can test our ideas. In the real world, confounding factors and messy details obfuscate core principles. But in a thought experiment, we can strip away the noise and focus on the essence of the problem. Thought experiments remind us that some of the most profound insights and innovations start with a simple question: “What if?” So the next time you’re grappling with complexity, remember: The answer might just begin in the sandbox of your mind.

These things, however, are not enough to win the battle. Without them, you definitely won’t be successful, but on their own they are not sufficient to guarantee success. This concept is easily demonstrated in sports as well. To be successful at a professional level in any sport depends on some necessary conditions: you must be physically capable of meeting the demands of that sport, and have the time and means to train. Meeting these conditions, however, is not sufficient to guarantee a successful outcome. Many hardworking, talented athletes are unable to break into the professional ranks.

Technology is fine, but the scientists and engineers only partially think through their problems. They solve certain aspects, but not the total, and as a consequence it is slapping us back in the face very hard. —Barbara McClintock[1]

Almost everyone can anticipate the immediate results of their actions. However, few people think about what happens next. First-order thinking is easy and common. Second-order thinking is harder and requires thinking further ahead and thinking holistically. It requires us to consider not only our actions and their immediate consequences, but the subsequent effects of those actions as well. Failing to consider the second- and third-order effects of our decisions can unleash disaster. First-order thinking is almost always about satisfying the immediate problem. Second-order thinking, on the other hand, avoids problems before they happen by asking, “And then what?” Without second-order thinking, it can be hard to appreciate just how often what appears to solve the immediate problem takes you further away from your objective. First-order thinking tells you the chocolate bar tastes good and will satisfy your cravings. Second-order thinking tells you that when the sugar high wears off, you’ll crash.

The British are a well-intentioned nation with an ample supply of smart politicians. However, during its colonial rule of India, the British government began to worry about the number of venomous cobras in Delhi. To reduce the population, they instituted a reward for every dead snake brought to officials. In response, Indian citizens dutifully began breeding the snakes to slaughter and bring to officials. The snake problem became worse than when the government first intervened, because the British officials didn’t think at the second level.

Stupidity is the same as evil if you judge by the results. —Margaret Atwood[2]

Things are not produced and consumed in a vacuum.

When we try to pick out anything by itself, we find it hitched to everything else in the Universe. —John Muir[4]

Second-order thinking is not a way to predict the future. You are only able to think of the likely consequences of your decisions’ consequences based on the information available to you. However, this is not an excuse to power ahead and wait for post facto scientific analysis.

Warren Buffett used a very apt metaphor once to describe the second-order problem, likening it to a crowd at a parade: Once a few people decide to stand on their tiptoes to see better, everyone has to stand on their tiptoes. No one can see any better, but they’re all worse off.[5]

Thinking long-term eliminates a lot of poor behavior.

Second-order thinking involves asking ourselves if what we are doing now is moving us closer to or further away from our objectives.

The most dangerous form of short-term thinking is one that doesn’t understand that just because results are not visible doesn’t mean they are not accumulating. Thinking long-term helps us see how the accumulation of tiny gains or losses moves us toward or away from our intended future.

Being aware of second-order consequences and using them to guide your decision making may mean the short term is less spectacular, but the payoffs for the long term can be enormous. By delaying gratification now, you will save time in the future. You won’t have to clean up the mess you made on account of not thinking through the effects of indulging your short-term desires.

Developing Trust for Future Success Trust and a sense of trustworthiness are the results of multiple interactions. This is why second-order thinking is so useful and valuable. Going for the immediate payoff in our interactions with people, unless the result is a win-win, almost always guarantees that interaction will be a one-off. Maximizing benefits is something that becomes possible only over time. Thus, considering the effects of the effects of our actions on others, or on our reputations, is critical to getting people to trust us and to enjoying the benefits of cooperation that come with that trust.[9]

Second-order thinking is a method of thinking that goes beyond the surface level, beyond the knee-jerk reactions and short-term gains. It asks us to play the long game, to anticipate the ripple effects of our actions and to make choices that will benefit us not just today, but for years to come. Second-order thinking demands we ask: “And then what?”

The theory of probability is the only mathematical tool available to help map the unknown and the uncontrollable. It is fortunate that this tool, while tricky, is extraordinarily powerful and convenient. —Benoit Mandelbrot[1]

Probabilistic thinking is essentially trying to estimate, using some math and logic, the likelihood of any specific outcome occurring. It is one of the best tools we have to improve the accuracy of our decisions. In a world where each moment is determined by an infinitely complex set of factors, probabilistic thinking helps us deal with uncertainty. When we know these, our decisions can be more precise and effective.

Thus, using conditional probability means being very careful to observe the conditions preceding an event you’d like to understand.

adjust probabilities when we encounter new data. The core of Bayesian thinking (or Bayesian updating, as it can be called) is this: given that we have limited but useful information about the world, and are constantly encountering new information, we should consider what we already know—as much of it as possible—when we learn something new.

At first glance, the two figures seem similar enough. Common outcomes cluster together, creating a wave. The difference is in the tails. In a bell curve, the extremes are predictable. There can only be so much deviation from the mean. In a fat-tailed curve, there is no real cap on extreme events. The more extreme events that are possible, the longer the tails of the curve get. Any one extreme event is still unlikely, but the sheer number of options means that we can’t rely on the most common outcomes as representing the average. The more extreme events that are possible, the higher the probability that one of them will occur. Crazy things are going to happen, and we have no way of identifying when.

Think of it this way: In a bell curve situation, such as displaying the distribution of heights or weights in a human population, there are outliers on the spectrum of possibility, but the outliers have a fairly well-defined scope. You’ll never meet a man who is ten times the size of an average man. But in a curve with fat tails, like wealth, the central tendency does not work the same way. You may regularly meet people who are ten, a hundred, or ten thousand times wealthier than the average person. That is a very different type of world.

The first one is what Wall Street traders would call “upside optionality”—that is, seeking out situations that we expect to have good odds of offering us opportunities. Take the example of attending a cocktail party where a lot of people you might like to know are in attendance. While nothing is guaranteed to happen—you may not meet those people, and if you do, it may not go well—you give yourself the benefit of serendipity and randomness. The worst thing that can happen is…nothing. One thing you know for sure is that you’ll never meet these people sitting at home. By going to the party, you improve your odds of encountering opportunity—your upside optionality. The second thing we can do is to learn how to fail properly. Failing properly has two major components: First, never take a risk that will do you in—never get taken out of the game completely. Second, develop the personal resilience to learn from your failures and start again. If you follow these two rules, you can only fail temporarily. No one likes to fail. It hurts. But failure carries with it one huge antifragile gift: learning. Those who are not afraid to fail (properly) have a huge advantage over the rest. What they learn makes them less vulnerable to the volatility of the world. They benefit from it, in true antifragile fashion.

The Antifragile mindset is a unique one. Whenever possible, try to create scenarios where randomness and uncertainty are your friends, not your enemies.

Another common asymmetry is people’s ability to estimate the effect of traffic on travel time. How often do you leave “on time” and arrive 20 percent early? Almost never? How often do you leave “on time” and arrive 20 percent late? All the time? Exactly. Your estimation errors are asymmetric, skewing in a single direction. This is often the case with probabilistic decision making.[5]

High-stakes survival situations tend to make us evaluate our environment with as little bias as possible.

Probabilistic thinking can only get you in the ballpark. It doesn’t guarantee 100 percent success.

Probabilistic thinking is the art of navigating uncertainty. Successfully thinking in shades of probability means identifying crucial factors, estimating the odds, challenging assumptions, and making informed decisions. The challenge of probabilistic thinking is that it requires constant updating. As new information emerges, the probabilities change. What seemed likely yesterday may seem unlikely today. This explains why probabilistic thinkers continuously revise their beliefs and why many find it uncomfortable.

It’s much easier to believe something false is true than to wrestle with uncertainty. Being a probabilistic thinker means being willing to say, “Based on the evidence, I think there’s about a 60 percent chance of X happening.” The rewards of probabilistic thinking are immense. By embracing uncertainty, we can make better decisions, avoid overconfidence, and navigate complex situations with greater skill and adaptability. We become more open-minded, more receptive to new ideas, and more resilient in the face of change.

You can always say yes to insuring, but the trick is to come up with the right price. For that, we need probability.

The test of a first-rate intelligence is the ability to hold two opposing ideas in mind at the same time and still retain the ability to function. One should, for example, be able to see that things are hopeless yet be determined to make them otherwise. —F. Scott Fitzgerald[1]

It can be difficult to appreciate just how much avoiding the standard ways of failing dramatically increases the odds of success.

Most of us tend to think one way about a problem: forward. Inversion allows us to flip the problem around and think backward. Sometimes it’s good to start at the beginning, but it can be more useful to start at the end.

Avoiding stupidity is easier than seeking brilliance. Even when we don’t know how to achieve a particular objective, we can often identify what prevents it from happening.

There are two approaches to applying inversion in your life: Start by assuming that what you’re trying to prove is either true or false, then show what else would have to be true to make that so. Instead of aiming directly for your goal, think deeply about what to avoid and then see what options are left over.

Decide what to avoid: Instead of thinking through the achievement of a positive outcome, another way to use inversion is to ask ourselves how we might achieve a terrible outcome, and let that guide our decision making.

Identify the problem. Define your objective. Identify the forces that support change toward your objective. Identify the forces that impede change toward the objective. Strategize a solution! This may involve both augmenting, or adding to, the forces in step 3 and reducing or eliminating the forces in step 4. Even if we are quite logical, most of us stop after step 3. Once we figure out our objective, we focus on the things we need to put in place to make it happen—the new training or education, the messaging and marketing. But Lewin theorized that it can be just as powerful to remove obstacles to change.

A lot of advantage is gained simply by avoiding the standard paths to failure. Inversion is not the way we are taught to think. We are taught to identify what we want and explore things that will move us closer to our objective. However, by considering what would guarantee failure, we significantly increase the odds of success. We often become so focused on solving a problem in a specific way that we overlook simpler, more effective solutions. Inversion forces us to consider the opposite side of the equation. Instead of asking, “How do I solve this problem?” inversion asks, “What would guarantee failure?” Instead of asking, “How can I achieve this goal?” it asks, “What is preventing me from achieving it?” By inverting the question, we can gain insights that our normal thought patterns might miss.

Anybody can make the simple complicated. Creativity is making the complicated simple. —Charles Mingus[1]

Simpler explanations are more likely to be true than complicated ones. This is the essence of Occam’s razor, a classic principle of logic and problem solving. Instead of wasting your time trying to disprove complex scenarios, you can make decisions more confidently by basing them on the explanation that has the fewest moving parts. The more complicated the explanation for something, the more skeptical you should be.

complexity takes work to unravel, manage, and understand.

Simple explanations are easier to falsify, easier to understand, and generally more likely to be correct. Occam’s razor is not an iron law but a tendency and a mindset you can choose to use: if all else is equal—that is, if two competing models both have equal explanatory power—it’s more likely that the simple solution suffices.

Carl Sagan wrote that “extraordinary claims require extraordinary proof.”[11]

Simple as we wish things were, irreducible complexity, like simplicity, is a part of our reality. Therefore, we can’t use Occam’s razor to create artificial simplicity. If something cannot be broken down any further, we must deal with it as it is.

How do you know something is as simple as it can be? Think of computer code. Code can sometimes be excessively complex. In trying to simplify it, we would have to make sure it can still perform the functions we need it to. This is one way to understand simplicity: an explanation can be simplified only to the extent that it can still provide an accurate understanding.

Occam’s razor is the intellectual equivalent of “keep it simple.” When faced with competing explanations or solutions, Occam’s razor suggests that the simplest explanation is often the most plausible. This doesn’t mean the simplest theory is always true, only that it should be preferred until proven otherwise. Sometimes, the truth is complex, and the simplest explanation doesn’t account for all the facts. Applying this powerful principle requires understanding when it works for you and when it doesn’t. A theory that is too simple may fail to capture reality, and one that is too complex risks collapsing under its own weight.

I need to listen well so that I hear what is not said. —Thuli Madonsela[1]

Hard to trace in its origin, Hanlon’s razor states that we should not attribute to malice that which is more easily explained by stupidity. In a complex world, using this model helps us avoid paranoia and ideology. By not generally assuming that bad results are the fault of a bad actor, we look for options instead of missing opportunities. This model reminds us that people do make mistakes and demands that we ask if there is another reasonable explanation for the events that have occurred. The explanation most likely to be right is the one that contains the least amount of intent.

We’re deeply affected by vivid, available evidence, to such a degree that we’re willing to make judgments that violate simple logic. We overconclude based on the available information; we have no trouble packaging in unrelated factors if they happen to occur in proximity to what we already believe.

When we see something happen that we don’t like and that seems wrong, we assume it’s intentional. But it’s more likely that it’s completely unintentional. Assuming someone is doing wrong and doing it purposefully is like assuming Linda is more likely to be a bank teller and a feminist.

Most people doing wrong are not bad people trying to be malicious.

Failing to prioritize stupidity over malice causes things like paranoia.

One is tempted to define man as a rational animal who always loses his temper when he is called upon to act in accordance with the dictates of reason. —Oscar Wilde[3]

As useful as Hanlon’s razor can be, however, it is important not to overthink this model. Hanlon’s razor is meant to help us perceive stupidity, or error, and their inadvertent consequences. It says that of all possible motives behind an action, the ones that require the least amount of energy to execute (such as ignorance or laziness) are more likely to be at work than ones that require active malice.

Hanlon’s razor is a mental safeguard against attributing behavior to malice when incompetence is a more likely explanation. It’s a reminder that people are not out to get you and it’s best to assume good faith and resist the urge to assign sinister motives without overwhelming evidence.

This isn’t to say that genuine malice doesn’t exist. It does. But in most interactions, stupidity is a far more common explanation than malevolence. People make mistakes. They forget things. They speak without thinking. They prioritize short-term wins over long-term wins. They act on incomplete information. They fall prey to bias and prejudice. From the outside, these actions might appear like deliberate attacks, but the reality is far more mundane. The real power of Hanlon’s razor lies in how it shifts our perspective. When we assume stupidity rather than malice, we respond differently. Instead of getting defensive or lashing out, we approach the situation with empathy and clarity. For everyday frustrations and misunderstandings, Hanlon’s razor is a powerful reminder to approach problems with a spirit of generosity. It’s a way to reduce unnecessary drama and stress in our lives, and to find practical solutions instead of descending into blame and recrimination.